Overview

Dataset statistics

Number of variables48
Number of observations2175
Missing cells89
Missing cells (%)0.1%
Duplicate rows2
Duplicate rows (%)0.1%
Total size in memory7.1 MiB
Average record size in memory3.4 KiB

Variable types

Categorical41
Numeric6
Boolean1

Warnings

Emirate has constant value "Abu Dhabi" Constant
City has constant value "Abu Dhabi" Constant
Road has constant value "Internal road" Constant
Report type has constant value "Traffic accident with injuries" Constant
Computed has constant value "1" Constant
Computed2 has constant value "1" Constant
Dataset has 2 (0.1%) duplicate rows Duplicates
Date has a high cardinality: 706 distinct values High cardinality
Streets has a high cardinality: 130 distinct values High cardinality
Street has a high cardinality: 130 distinct values High cardinality
Nationalities has a high cardinality: 64 distinct values High cardinality
Iac Rep Time has a high cardinality: 535 distinct values High cardinality
Block has a high cardinality: 94 distinct values High cardinality
Accident Description has a high cardinality: 2061 distinct values High cardinality
Location.1 has a high cardinality: 1252 distinct values High cardinality
Age of the injured is highly correlated with Inp AgeHigh correlation
Inp Age is highly correlated with Age of the injuredHigh correlation
Place is highly correlated with Report type and 5 other fieldsHigh correlation
Report type is highly correlated with Place and 34 other fieldsHigh correlation
Year is highly correlated with Report type and 5 other fieldsHigh correlation
Police Station is highly correlated with Report type and 5 other fieldsHigh correlation
Fasten seat belt is highly correlated with Report type and 6 other fieldsHigh correlation
Age Group is highly correlated with Report type and 6 other fieldsHigh correlation
Nationality of the Injured person is highly correlated with Report type and 6 other fieldsHigh correlation
Computed is highly correlated with Place and 34 other fieldsHigh correlation
Computed2 is highly correlated with Place and 34 other fieldsHigh correlation
Nationalities is highly correlated with Report type and 6 other fieldsHigh correlation
Date - Month is highly correlated with Report type and 6 other fieldsHigh correlation
Injured person position is highly correlated with Report type and 6 other fieldsHigh correlation
lighting is highly correlated with Report type and 5 other fieldsHigh correlation
Pedestrian action is highly correlated with Report type and 5 other fieldsHigh correlation
Degree of the injury is highly correlated with Report type and 5 other fieldsHigh correlation
City is highly correlated with Place and 34 other fieldsHigh correlation
Location is highly correlated with Report type and 5 other fieldsHigh correlation
Report Number is highly correlated with Report type and 5 other fieldsHigh correlation
Week is highly correlated with Report type and 5 other fieldsHigh correlation
Block is highly correlated with Report type and 5 other fieldsHigh correlation
Emirate is highly correlated with Place and 34 other fieldsHigh correlation
Age Group - Ministry is highly correlated with Report type and 6 other fieldsHigh correlation
Day.1 is highly correlated with Report type and 5 other fieldsHigh correlation
Road is highly correlated with Place and 34 other fieldsHigh correlation
Time is highly correlated with Report type and 5 other fieldsHigh correlation
Intersection is highly correlated with Report type and 5 other fieldsHigh correlation
Injured person's seat is highly correlated with Report type and 6 other fieldsHigh correlation
Reasons is highly correlated with Report type and 5 other fieldsHigh correlation
Month is highly correlated with Report type and 6 other fieldsHigh correlation
Road surface is highly correlated with Report type and 5 other fieldsHigh correlation
Seat Belt is highly correlated with Report type and 6 other fieldsHigh correlation
Gender of the injured is highly correlated with Report type and 6 other fieldsHigh correlation
Area is highly correlated with Report type and 5 other fieldsHigh correlation
Accident Type is highly correlated with Report type and 5 other fieldsHigh correlation
Weather is highly correlated with Report type and 5 other fieldsHigh correlation
Gender is highly correlated with Report type and 6 other fieldsHigh correlation
Number of Lanes has 78 (3.6%) missing values Missing
Accident Description is uniformly distributed Uniform

Reproduction

Analysis started2021-02-28 15:17:03.518945
Analysis finished2021-02-28 15:17:50.282405
Duration46.76 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Emirate
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.3 KiB
Abu Dhabi
2175 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters19575
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAbu Dhabi
2nd rowAbu Dhabi
3rd rowAbu Dhabi
4th rowAbu Dhabi
5th rowAbu Dhabi
ValueCountFrequency (%)
Abu Dhabi2175
100.0%
2021-02-28T18:17:50.657604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:17:50.771540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
dhabi2175
50.0%
abu2175
50.0%

Most occurring characters

ValueCountFrequency (%)
b4350
22.2%
A2175
11.1%
u2175
11.1%
2175
11.1%
D2175
11.1%
h2175
11.1%
a2175
11.1%
i2175
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13050
66.7%
Uppercase Letter4350
 
22.2%
Space Separator2175
 
11.1%

Most frequent character per category

ValueCountFrequency (%)
b4350
33.3%
u2175
16.7%
h2175
16.7%
a2175
16.7%
i2175
16.7%
ValueCountFrequency (%)
A2175
50.0%
D2175
50.0%
ValueCountFrequency (%)
2175
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17400
88.9%
Common2175
 
11.1%

Most frequent character per script

ValueCountFrequency (%)
b4350
25.0%
A2175
12.5%
u2175
12.5%
D2175
12.5%
h2175
12.5%
a2175
12.5%
i2175
12.5%
ValueCountFrequency (%)
2175
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII19575
100.0%

Most frequent character per block

ValueCountFrequency (%)
b4350
22.2%
A2175
11.1%
u2175
11.1%
2175
11.1%
D2175
11.1%
h2175
11.1%
a2175
11.1%
i2175
11.1%

Police Station
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size170.3 KiB
Khaldiya Police Station
1020 
Shaabiya Police Station
903 
Al Madina Police Station
252 

Length

Max length24
Median length23
Mean length23.11586207
Min length23

Characters and Unicode

Total characters50277
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShaabiya Police Station
2nd rowShaabiya Police Station
3rd rowAl Madina Police Station
4th rowKhaldiya Police Station
5th rowKhaldiya Police Station
ValueCountFrequency (%)
Khaldiya Police Station1020
46.9%
Shaabiya Police Station903
41.5%
Al Madina Police Station252
 
11.6%
2021-02-28T18:17:51.110347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:17:51.228300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
police2175
32.1%
station2175
32.1%
khaldiya1020
15.1%
shaabiya903
13.3%
al252
 
3.7%
madina252
 
3.7%

Most occurring characters

ValueCountFrequency (%)
a7428
14.8%
i6525
13.0%
4602
9.2%
o4350
8.7%
t4350
8.7%
l3447
 
6.9%
S3078
 
6.1%
n2427
 
4.8%
P2175
 
4.3%
c2175
 
4.3%
Other values (8)9720
19.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter38898
77.4%
Uppercase Letter6777
 
13.5%
Space Separator4602
 
9.2%

Most frequent character per category

ValueCountFrequency (%)
a7428
19.1%
i6525
16.8%
o4350
11.2%
t4350
11.2%
l3447
8.9%
n2427
 
6.2%
c2175
 
5.6%
e2175
 
5.6%
h1923
 
4.9%
y1923
 
4.9%
Other values (2)2175
 
5.6%
ValueCountFrequency (%)
S3078
45.4%
P2175
32.1%
K1020
 
15.1%
A252
 
3.7%
M252
 
3.7%
ValueCountFrequency (%)
4602
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin45675
90.8%
Common4602
 
9.2%

Most frequent character per script

ValueCountFrequency (%)
a7428
16.3%
i6525
14.3%
o4350
9.5%
t4350
9.5%
l3447
7.5%
S3078
6.7%
n2427
 
5.3%
P2175
 
4.8%
c2175
 
4.8%
e2175
 
4.8%
Other values (7)7545
16.5%
ValueCountFrequency (%)
4602
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII50277
100.0%

Most frequent character per block

ValueCountFrequency (%)
a7428
14.8%
i6525
13.0%
4602
9.2%
o4350
8.7%
t4350
8.7%
l3447
 
6.9%
S3078
 
6.1%
n2427
 
4.8%
P2175
 
4.3%
c2175
 
4.3%
Other values (8)9720
19.3%

Year
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size129.7 KiB
2012
886 
2011
824 
2013
465 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters8700
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2011
2nd row2011
3rd row2011
4th row2011
5th row2011
ValueCountFrequency (%)
2012886
40.7%
2011824
37.9%
2013465
21.4%
2021-02-28T18:17:51.719999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:17:51.852922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2012886
40.7%
2011824
37.9%
2013465
21.4%

Most occurring characters

ValueCountFrequency (%)
23061
35.2%
12999
34.5%
02175
25.0%
3465
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8700
100.0%

Most frequent character per category

ValueCountFrequency (%)
23061
35.2%
12999
34.5%
02175
25.0%
3465
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common8700
100.0%

Most frequent character per script

ValueCountFrequency (%)
23061
35.2%
12999
34.5%
02175
25.0%
3465
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII8700
100.0%

Most frequent character per block

ValueCountFrequency (%)
23061
35.2%
12999
34.5%
02175
25.0%
3465
 
5.3%

Month
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size134.1 KiB
March
264 
January
236 
April
209 
May
200 
February
190 
Other values (7)
1076 

Length

Max length9
Median length6
Mean length6.060229885
Min length3

Characters and Unicode

Total characters13181
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJanuary
2nd rowJanuary
3rd rowJanuary
4th rowJanuary
5th rowJanuary
ValueCountFrequency (%)
March264
12.1%
January236
10.9%
April209
9.6%
May200
9.2%
February190
8.7%
June188
8.6%
August158
7.3%
December154
7.1%
September153
7.0%
October149
6.9%
Other values (2)274
12.6%
2021-02-28T18:17:52.183703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
march264
12.1%
january236
10.9%
april209
9.6%
may200
9.2%
february190
8.7%
june188
8.6%
august158
7.3%
december154
7.1%
september153
7.0%
october149
6.9%
Other values (2)274
12.6%

Most occurring characters

ValueCountFrequency (%)
e1746
13.2%
r1694
12.9%
a1126
 
8.5%
u1055
 
8.0%
b795
 
6.0%
y751
 
5.7%
c567
 
4.3%
J549
 
4.2%
M464
 
3.5%
t460
 
3.5%
Other values (16)3974
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11006
83.5%
Uppercase Letter2175
 
16.5%

Most frequent character per category

ValueCountFrequency (%)
e1746
15.9%
r1694
15.4%
a1126
10.2%
u1055
9.6%
b795
7.2%
y751
 
6.8%
c567
 
5.2%
t460
 
4.2%
m456
 
4.1%
n424
 
3.9%
Other values (8)1932
17.6%
ValueCountFrequency (%)
J549
25.2%
M464
21.3%
A367
16.9%
F190
 
8.7%
D154
 
7.1%
S153
 
7.0%
O149
 
6.9%
N149
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Latin13181
100.0%

Most frequent character per script

ValueCountFrequency (%)
e1746
13.2%
r1694
12.9%
a1126
 
8.5%
u1055
 
8.0%
b795
 
6.0%
y751
 
5.7%
c567
 
4.3%
J549
 
4.2%
M464
 
3.5%
t460
 
3.5%
Other values (16)3974
30.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII13181
100.0%

Most frequent character per block

ValueCountFrequency (%)
e1746
13.2%
r1694
12.9%
a1126
 
8.5%
u1055
 
8.0%
b795
 
6.0%
y751
 
5.7%
c567
 
4.3%
J549
 
4.2%
M464
 
3.5%
t460
 
3.5%
Other values (16)3974
30.1%

Date
Categorical

HIGH CARDINALITY

Distinct706
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Memory size138.2 KiB
15-07-12
 
17
03-01-11
 
16
28-12-12
 
12
16-03-12
 
12
16-02-13
 
12
Other values (701)
2106 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters17400
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique215 ?
Unique (%)9.9%

Sample

1st row01-01-11
2nd row01-01-11
3rd row01-01-11
4th row01-01-11
5th row01-01-11
ValueCountFrequency (%)
15-07-1217
 
0.8%
03-01-1116
 
0.7%
28-12-1212
 
0.6%
16-03-1212
 
0.6%
16-02-1312
 
0.6%
12-04-1212
 
0.6%
05-01-1111
 
0.5%
28-10-1211
 
0.5%
12-04-1311
 
0.5%
14-01-1311
 
0.5%
Other values (696)2050
94.3%
2021-02-28T18:17:52.653633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
15-07-1217
 
0.8%
03-01-1116
 
0.7%
28-12-1212
 
0.6%
16-03-1212
 
0.6%
16-02-1312
 
0.6%
12-04-1212
 
0.6%
05-01-1111
 
0.5%
28-10-1211
 
0.5%
12-04-1311
 
0.5%
14-01-1311
 
0.5%
Other values (696)2050
94.3%

Most occurring characters

ValueCountFrequency (%)
14805
27.6%
-4350
25.0%
02793
16.1%
22061
11.8%
31093
 
6.3%
5428
 
2.5%
4416
 
2.4%
6408
 
2.3%
8362
 
2.1%
9355
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number13050
75.0%
Dash Punctuation4350
 
25.0%

Most frequent character per category

ValueCountFrequency (%)
14805
36.8%
02793
21.4%
22061
15.8%
31093
 
8.4%
5428
 
3.3%
4416
 
3.2%
6408
 
3.1%
8362
 
2.8%
9355
 
2.7%
7329
 
2.5%
ValueCountFrequency (%)
-4350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common17400
100.0%

Most frequent character per script

ValueCountFrequency (%)
14805
27.6%
-4350
25.0%
02793
16.1%
22061
11.8%
31093
 
6.3%
5428
 
2.5%
4416
 
2.4%
6408
 
2.3%
8362
 
2.1%
9355
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII17400
100.0%

Most frequent character per block

ValueCountFrequency (%)
14805
27.6%
-4350
25.0%
02793
16.1%
22061
11.8%
31093
 
6.3%
5428
 
2.5%
4416
 
2.4%
6408
 
2.3%
8362
 
2.1%
9355
 
2.0%

City
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.3 KiB
Abu Dhabi
2175 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters19575
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAbu Dhabi
2nd rowAbu Dhabi
3rd rowAbu Dhabi
4th rowAbu Dhabi
5th rowAbu Dhabi
ValueCountFrequency (%)
Abu Dhabi2175
100.0%
2021-02-28T18:17:53.058401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:17:53.162343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
dhabi2175
50.0%
abu2175
50.0%

Most occurring characters

ValueCountFrequency (%)
b4350
22.2%
A2175
11.1%
u2175
11.1%
2175
11.1%
D2175
11.1%
h2175
11.1%
a2175
11.1%
i2175
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13050
66.7%
Uppercase Letter4350
 
22.2%
Space Separator2175
 
11.1%

Most frequent character per category

ValueCountFrequency (%)
b4350
33.3%
u2175
16.7%
h2175
16.7%
a2175
16.7%
i2175
16.7%
ValueCountFrequency (%)
A2175
50.0%
D2175
50.0%
ValueCountFrequency (%)
2175
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17400
88.9%
Common2175
 
11.1%

Most frequent character per script

ValueCountFrequency (%)
b4350
25.0%
A2175
12.5%
u2175
12.5%
D2175
12.5%
h2175
12.5%
a2175
12.5%
i2175
12.5%
ValueCountFrequency (%)
2175
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII19575
100.0%

Most frequent character per block

ValueCountFrequency (%)
b4350
22.2%
A2175
11.1%
u2175
11.1%
2175
11.1%
D2175
11.1%
h2175
11.1%
a2175
11.1%
i2175
11.1%

Road
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size148.8 KiB
Internal road
2175 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters28275
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInternal road
2nd rowInternal road
3rd rowInternal road
4th rowInternal road
5th rowInternal road
ValueCountFrequency (%)
Internal road2175
100.0%
2021-02-28T18:17:53.453176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:17:53.569111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
internal2175
50.0%
road2175
50.0%

Most occurring characters

ValueCountFrequency (%)
n4350
15.4%
r4350
15.4%
a4350
15.4%
I2175
7.7%
t2175
7.7%
e2175
7.7%
l2175
7.7%
2175
7.7%
o2175
7.7%
d2175
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter23925
84.6%
Uppercase Letter2175
 
7.7%
Space Separator2175
 
7.7%

Most frequent character per category

ValueCountFrequency (%)
n4350
18.2%
r4350
18.2%
a4350
18.2%
t2175
9.1%
e2175
9.1%
l2175
9.1%
o2175
9.1%
d2175
9.1%
ValueCountFrequency (%)
I2175
100.0%
ValueCountFrequency (%)
2175
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin26100
92.3%
Common2175
 
7.7%

Most frequent character per script

ValueCountFrequency (%)
n4350
16.7%
r4350
16.7%
a4350
16.7%
I2175
8.3%
t2175
8.3%
e2175
8.3%
l2175
8.3%
o2175
8.3%
d2175
8.3%
ValueCountFrequency (%)
2175
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII28275
100.0%

Most frequent character per block

ValueCountFrequency (%)
n4350
15.4%
r4350
15.4%
a4350
15.4%
I2175
7.7%
t2175
7.7%
e2175
7.7%
l2175
7.7%
2175
7.7%
o2175
7.7%
d2175
7.7%

Report Number
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size150.9 KiB
100000000000.0
2139 
912000000000.0
 
11
114000000000.0
 
10
112000000000.0
 
9
115000000000.0
 
6

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters30450
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100000000000.0
2nd row100000000000.0
3rd row100000000000.0
4th row100000000000.0
5th row100000000000.0
ValueCountFrequency (%)
100000000000.02139
98.3%
912000000000.011
 
0.5%
114000000000.010
 
0.5%
112000000000.09
 
0.4%
115000000000.06
 
0.3%
2021-02-28T18:17:53.881933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:17:54.018854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
100000000000.02139
98.3%
912000000000.011
 
0.5%
114000000000.010
 
0.5%
112000000000.09
 
0.4%
115000000000.06
 
0.3%

Most occurring characters

ValueCountFrequency (%)
026028
85.5%
12200
 
7.2%
.2175
 
7.1%
220
 
0.1%
911
 
< 0.1%
410
 
< 0.1%
56
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28275
92.9%
Other Punctuation2175
 
7.1%

Most frequent character per category

ValueCountFrequency (%)
026028
92.1%
12200
 
7.8%
220
 
0.1%
911
 
< 0.1%
410
 
< 0.1%
56
 
< 0.1%
ValueCountFrequency (%)
.2175
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30450
100.0%

Most frequent character per script

ValueCountFrequency (%)
026028
85.5%
12200
 
7.2%
.2175
 
7.1%
220
 
0.1%
911
 
< 0.1%
410
 
< 0.1%
56
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30450
100.0%

Most frequent character per block

ValueCountFrequency (%)
026028
85.5%
12200
 
7.2%
.2175
 
7.1%
220
 
0.1%
911
 
< 0.1%
410
 
< 0.1%
56
 
< 0.1%

Day
Real number (ℝ≥0)

Distinct31
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.21931034
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size17.1 KiB
2021-02-28T18:17:54.152393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median15
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.900409554
Coefficient of variation (CV)0.5848103069
Kurtosis-1.178917132
Mean15.21931034
Median Absolute Deviation (MAD)8
Skewness0.1281609189
Sum33102
Variance79.21729023
MonotocityNot monotonic
2021-02-28T18:17:54.376780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3115
 
5.3%
1694
 
4.3%
594
 
4.3%
1488
 
4.0%
784
 
3.9%
1078
 
3.6%
2078
 
3.6%
977
 
3.5%
2976
 
3.5%
2875
 
3.4%
Other values (21)1316
60.5%
ValueCountFrequency (%)
172
3.3%
262
2.9%
3115
5.3%
458
2.7%
594
4.3%
664
2.9%
784
3.9%
870
3.2%
977
3.5%
1078
3.6%
ValueCountFrequency (%)
3148
2.2%
3069
3.2%
2976
3.5%
2875
3.4%
2753
2.4%
2662
2.9%
2560
2.8%
2461
2.8%
2362
2.9%
2253
2.4%

Week
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Memory size143.0 KiB
First week
1207 
Second week
321 
Third week
289 
Fourth week
246 
Fifth week
 
111

Length

Max length11
Median length10
Mean length10.26080957
Min length10

Characters and Unicode

Total characters22307
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirst week
2nd rowFirst week
3rd rowFirst week
4th rowFirst week
5th rowFirst week
ValueCountFrequency (%)
First week1207
55.5%
Second week321
 
14.8%
Third week289
 
13.3%
Fourth week246
 
11.3%
Fifth week111
 
5.1%
(Missing)1
 
< 0.1%
2021-02-28T18:17:54.853491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:17:54.981418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
week2174
50.0%
first1207
27.8%
second321
 
7.4%
third289
 
6.6%
fourth246
 
5.7%
fifth111
 
2.6%

Most occurring characters

ValueCountFrequency (%)
e4669
20.9%
2174
9.7%
w2174
9.7%
k2174
9.7%
r1742
 
7.8%
i1607
 
7.2%
F1564
 
7.0%
t1564
 
7.0%
s1207
 
5.4%
h646
 
2.9%
Other values (8)2786
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter17959
80.5%
Uppercase Letter2174
 
9.7%
Space Separator2174
 
9.7%

Most frequent character per category

ValueCountFrequency (%)
e4669
26.0%
w2174
12.1%
k2174
12.1%
r1742
 
9.7%
i1607
 
8.9%
t1564
 
8.7%
s1207
 
6.7%
h646
 
3.6%
d610
 
3.4%
o567
 
3.2%
Other values (4)999
 
5.6%
ValueCountFrequency (%)
F1564
71.9%
S321
 
14.8%
T289
 
13.3%
ValueCountFrequency (%)
2174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin20133
90.3%
Common2174
 
9.7%

Most frequent character per script

ValueCountFrequency (%)
e4669
23.2%
w2174
10.8%
k2174
10.8%
r1742
 
8.7%
i1607
 
8.0%
F1564
 
7.8%
t1564
 
7.8%
s1207
 
6.0%
h646
 
3.2%
d610
 
3.0%
Other values (7)2176
10.8%
ValueCountFrequency (%)
2174
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII22307
100.0%

Most frequent character per block

ValueCountFrequency (%)
e4669
20.9%
2174
9.7%
w2174
9.7%
k2174
9.7%
r1742
 
7.8%
i1607
 
7.2%
F1564
 
7.0%
t1564
 
7.0%
s1207
 
5.4%
h646
 
2.9%
Other values (8)2786
12.5%

Report type
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size184.9 KiB
Traffic accident with injuries
2175 

Length

Max length30
Median length30
Mean length30
Min length30

Characters and Unicode

Total characters65250
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTraffic accident with injuries
2nd rowTraffic accident with injuries
3rd rowTraffic accident with injuries
4th rowTraffic accident with injuries
5th rowTraffic accident with injuries
ValueCountFrequency (%)
Traffic accident with injuries2175
100.0%
2021-02-28T18:17:55.340230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:17:55.441156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
traffic2175
25.0%
injuries2175
25.0%
with2175
25.0%
accident2175
25.0%

Most occurring characters

ValueCountFrequency (%)
i10875
16.7%
c6525
10.0%
6525
10.0%
r4350
 
6.7%
a4350
 
6.7%
f4350
 
6.7%
e4350
 
6.7%
n4350
 
6.7%
t4350
 
6.7%
T2175
 
3.3%
Other values (6)13050
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter56550
86.7%
Space Separator6525
 
10.0%
Uppercase Letter2175
 
3.3%

Most frequent character per category

ValueCountFrequency (%)
i10875
19.2%
c6525
11.5%
r4350
 
7.7%
a4350
 
7.7%
f4350
 
7.7%
e4350
 
7.7%
n4350
 
7.7%
t4350
 
7.7%
d2175
 
3.8%
w2175
 
3.8%
Other values (4)8700
15.4%
ValueCountFrequency (%)
T2175
100.0%
ValueCountFrequency (%)
6525
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin58725
90.0%
Common6525
 
10.0%

Most frequent character per script

ValueCountFrequency (%)
i10875
18.5%
c6525
11.1%
r4350
 
7.4%
a4350
 
7.4%
f4350
 
7.4%
e4350
 
7.4%
n4350
 
7.4%
t4350
 
7.4%
T2175
 
3.7%
d2175
 
3.7%
Other values (5)10875
18.5%
ValueCountFrequency (%)
6525
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII65250
100.0%

Most frequent character per block

ValueCountFrequency (%)
i10875
16.7%
c6525
10.0%
6525
10.0%
r4350
 
6.7%
a4350
 
6.7%
f4350
 
6.7%
e4350
 
6.7%
n4350
 
6.7%
t4350
 
6.7%
T2175
 
3.3%
Other values (6)13050
20.0%

Reasons
Categorical

HIGH CORRELATION

Distinct26
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size189.0 KiB
Bypassing the red traffic light
935 
Speedy without taking road conditions into account
347 
Not leaving enough space
192 
Misuse of traffic
153 
Ignoring road lane
135 
Other values (21)
413 

Length

Max length51
Median length31
Mean length31.91310345
Min length10

Characters and Unicode

Total characters69411
Distinct characters38
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.2%

Sample

1st rowunder influence of intoxicant or narcotic drug
2nd rowunder influence of intoxicant or narcotic drug
3rd rowunder influence of intoxicant or narcotic drug
4th rowBypassing the red traffic light
5th rowNegligence and inobservance
ValueCountFrequency (%)
Bypassing the red traffic light935
43.0%
Speedy without taking road conditions into account347
 
16.0%
Not leaving enough space192
 
8.8%
Misuse of traffic153
 
7.0%
Ignoring road lane135
 
6.2%
under influence of intoxicant or narcotic drug118
 
5.4%
Sudden deviation62
 
2.9%
Not observing the stop sign40
 
1.8%
Not giving priority to pedestrian crossing31
 
1.4%
Negligence and inobservance30
 
1.4%
Other values (16)132
 
6.1%
2021-02-28T18:17:55.795954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
traffic1128
 
10.5%
the979
 
9.1%
bypassing935
 
8.7%
light935
 
8.7%
red935
 
8.7%
road507
 
4.7%
without391
 
3.6%
account347
 
3.2%
speedy347
 
3.2%
into347
 
3.2%
Other values (57)3882
36.2%

Most occurring characters

ValueCountFrequency (%)
8558
12.3%
i6530
 
9.4%
t6183
 
8.9%
n4879
 
7.0%
e4474
 
6.4%
a4361
 
6.3%
o3864
 
5.6%
r3534
 
5.1%
g3409
 
4.9%
c3056
 
4.4%
Other values (28)20563
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter58721
84.6%
Space Separator8558
 
12.3%
Uppercase Letter2055
 
3.0%
Other Punctuation75
 
0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
i6530
11.1%
t6183
10.5%
n4879
 
8.3%
e4474
 
7.6%
a4361
 
7.4%
o3864
 
6.6%
r3534
 
6.0%
g3409
 
5.8%
c3056
 
5.2%
s3048
 
5.2%
Other values (13)15383
26.2%
ValueCountFrequency (%)
B935
45.5%
S420
20.4%
N318
 
15.5%
M153
 
7.4%
I138
 
6.7%
E47
 
2.3%
D23
 
1.1%
L10
 
0.5%
F9
 
0.4%
T2
 
0.1%
ValueCountFrequency (%)
?59
78.7%
/16
 
21.3%
ValueCountFrequency (%)
8558
100.0%
ValueCountFrequency (%)
(1
100.0%
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin60776
87.6%
Common8635
 
12.4%

Most frequent character per script

ValueCountFrequency (%)
i6530
 
10.7%
t6183
 
10.2%
n4879
 
8.0%
e4474
 
7.4%
a4361
 
7.2%
o3864
 
6.4%
r3534
 
5.8%
g3409
 
5.6%
c3056
 
5.0%
s3048
 
5.0%
Other values (23)17438
28.7%
ValueCountFrequency (%)
8558
99.1%
?59
 
0.7%
/16
 
0.2%
(1
 
< 0.1%
)1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII69411
100.0%

Most frequent character per block

ValueCountFrequency (%)
8558
12.3%
i6530
 
9.4%
t6183
 
8.9%
n4879
 
7.0%
e4474
 
6.4%
a4361
 
6.3%
o3864
 
5.6%
r3534
 
5.1%
g3409
 
4.9%
c3056
 
4.4%
Other values (28)20563
29.6%

Computed
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.3 KiB
1
2175 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2175
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
12175
100.0%
2021-02-28T18:17:56.094508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:17:56.179181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
12175
100.0%

Most occurring characters

ValueCountFrequency (%)
12175
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2175
100.0%

Most frequent character per category

ValueCountFrequency (%)
12175
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2175
100.0%

Most frequent character per script

ValueCountFrequency (%)
12175
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2175
100.0%

Most frequent character per block

ValueCountFrequency (%)
12175
100.0%

Streets
Categorical

HIGH CARDINALITY

Distinct130
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size198.1 KiB
Al Sharqi Street (Muroor) (4)
 
157
Street Arabian Gulf(30)
 
144
Rashed Bin Saeed Al-Maktoum Street (2)
 
137
Street Salam (8)
 
94
Street Corniche(1)
 
65
Other values (125)
1578 

Length

Max length67
Median length38
Mean length36.19494253
Min length9

Characters and Unicode

Total characters78724
Distinct characters61
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)0.8%

Sample

1st rowRashid Bin Saeed Al Maktoum intersection - Street IP28A 27
2nd rowAl Sharqi intersection (Muroor) - Mohamed bin Khalifa IP49A
3rd rowStreet 10
4th rowRashed bin Saeed Al-Maktoum intersection - Street 29 IP76
5th rowKhalifa Bin Shakhbout Street (28)
ValueCountFrequency (%)
Al Sharqi Street (Muroor) (4)157
 
7.2%
Street Arabian Gulf(30)144
 
6.6%
Rashed Bin Saeed Al-Maktoum Street (2)137
 
6.3%
Street Salam (8)94
 
4.3%
Street Corniche(1)65
 
3.0%
Street Zayed Al Awal(7)63
 
2.9%
Street Falah(9)50
 
2.3%
Al Sharqi intersection (Muroor) - Mohamed bin Khalifa IP49A50
 
2.3%
IP75D IntersectionAl Sharqi (Muroor) - Street 3145
 
2.1%
Street Hamdan Bin Mohamed(5)45
 
2.1%
Other values (120)1325
60.9%
2021-02-28T18:17:56.670610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
street1435
 
12.2%
1041
 
8.8%
bin877
 
7.5%
al761
 
6.5%
saeed356
 
3.0%
muroor348
 
3.0%
sharqi348
 
3.0%
al-maktoum316
 
2.7%
awal274
 
2.3%
arabian274
 
2.3%
Other values (208)5734
48.7%

Most occurring characters

ValueCountFrequency (%)
9589
 
12.2%
e7162
 
9.1%
a6365
 
8.1%
t5579
 
7.1%
r4300
 
5.5%
n4018
 
5.1%
i3441
 
4.4%
l2780
 
3.5%
o2678
 
3.4%
S2615
 
3.3%
Other values (51)30197
38.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter47988
61.0%
Uppercase Letter12463
 
15.8%
Space Separator9589
 
12.2%
Decimal Number4219
 
5.4%
Open Punctuation1471
 
1.9%
Close Punctuation1471
 
1.9%
Dash Punctuation1348
 
1.7%
Other Punctuation175
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
e7162
14.9%
a6365
13.3%
t5579
11.6%
r4300
9.0%
n4018
8.4%
i3441
7.2%
l2780
 
5.8%
o2678
 
5.6%
d1818
 
3.8%
h1578
 
3.3%
Other values (14)8269
17.2%
ValueCountFrequency (%)
S2615
21.0%
A2278
18.3%
I2074
16.6%
P1114
8.9%
M1011
 
8.1%
B945
 
7.6%
Z446
 
3.6%
G375
 
3.0%
R373
 
3.0%
K299
 
2.4%
Other values (12)933
 
7.5%
ValueCountFrequency (%)
3696
16.5%
1674
16.0%
2577
13.7%
4446
10.6%
7393
9.3%
5376
8.9%
0326
7.7%
9284
6.7%
8242
 
5.7%
6205
 
4.9%
ValueCountFrequency (%)
9589
100.0%
ValueCountFrequency (%)
-1348
100.0%
ValueCountFrequency (%)
(1471
100.0%
ValueCountFrequency (%)
)1471
100.0%
ValueCountFrequency (%)
?175
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin60451
76.8%
Common18273
 
23.2%

Most frequent character per script

ValueCountFrequency (%)
e7162
 
11.8%
a6365
 
10.5%
t5579
 
9.2%
r4300
 
7.1%
n4018
 
6.6%
i3441
 
5.7%
l2780
 
4.6%
o2678
 
4.4%
S2615
 
4.3%
A2278
 
3.8%
Other values (36)19235
31.8%
ValueCountFrequency (%)
9589
52.5%
(1471
 
8.1%
)1471
 
8.1%
-1348
 
7.4%
3696
 
3.8%
1674
 
3.7%
2577
 
3.2%
4446
 
2.4%
7393
 
2.2%
5376
 
2.1%
Other values (5)1232
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII78724
100.0%

Most frequent character per block

ValueCountFrequency (%)
9589
 
12.2%
e7162
 
9.1%
a6365
 
8.1%
t5579
 
7.1%
r4300
 
5.5%
n4018
 
5.1%
i3441
 
4.4%
l2780
 
3.5%
o2678
 
3.4%
S2615
 
3.3%
Other values (51)30197
38.4%

Street
Categorical

HIGH CARDINALITY

Distinct130
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size197.9 KiB
Al Sharqi Street (Muroor) (4)
 
157
Street Arabian Gulf(30)
 
144
Rashed Bin Saeed Al-Maktoum Street (2)
 
137
Street Salam (8)
 
94
Street Corniche(1)
 
65
Other values (125)
1578 

Length

Max length67
Median length38
Mean length36.11402299
Min length9

Characters and Unicode

Total characters78548
Distinct characters61
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)0.8%

Sample

1st rowRashid Bin Saeed Al Maktoum intersection - Street IP28A 27
2nd rowAl Sharqi intersection (Muroor) - Mohamed bin Khalifa IP49A
3rd rowStreet 10
4th rowRashed bin Saeed Al-Maktoum intersection - Street 29 IP76
5th rowKhalifa Bin Shakhbout Street (28)
ValueCountFrequency (%)
Al Sharqi Street (Muroor) (4)157
 
7.2%
Street Arabian Gulf(30)144
 
6.6%
Rashed Bin Saeed Al-Maktoum Street (2)137
 
6.3%
Street Salam (8)94
 
4.3%
Street Corniche(1)65
 
3.0%
Street Zayed Al Awal(7)63
 
2.9%
Street Falah(9)50
 
2.3%
Al Sharqi intersection (Muroor) - Mohamed bin Khalifa IP49A50
 
2.3%
Street Hamdan Bin Mohamed(5)45
 
2.1%
IP75D IntersectionAl Sharqi (Muroor) - Street 3145
 
2.1%
Other values (120)1325
60.9%
2021-02-28T18:17:57.423202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
street1435
 
12.2%
1041
 
8.9%
bin877
 
7.5%
al761
 
6.5%
saeed356
 
3.0%
sharqi348
 
3.0%
muroor348
 
3.0%
al-maktoum316
 
2.7%
arabian274
 
2.3%
awal274
 
2.3%
Other values (205)5710
48.6%

Most occurring characters

ValueCountFrequency (%)
9565
 
12.2%
e7146
 
9.1%
a6365
 
8.1%
t5555
 
7.1%
r4276
 
5.4%
n4002
 
5.1%
i3425
 
4.4%
l2780
 
3.5%
o2662
 
3.4%
S2615
 
3.3%
Other values (51)30157
38.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter47836
60.9%
Uppercase Letter12463
 
15.9%
Space Separator9565
 
12.2%
Decimal Number4219
 
5.4%
Open Punctuation1471
 
1.9%
Close Punctuation1471
 
1.9%
Dash Punctuation1348
 
1.7%
Other Punctuation175
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
S2615
21.0%
A2278
18.3%
I2074
16.6%
P1114
8.9%
M1011
 
8.1%
B945
 
7.6%
Z446
 
3.6%
G375
 
3.0%
R373
 
3.0%
K299
 
2.4%
Other values (13)933
 
7.5%
ValueCountFrequency (%)
e7146
14.9%
a6365
13.3%
t5555
11.6%
r4276
8.9%
n4002
8.4%
i3425
7.2%
l2780
 
5.8%
o2662
 
5.6%
d1826
 
3.8%
h1578
 
3.3%
Other values (13)8221
17.2%
ValueCountFrequency (%)
3696
16.5%
1674
16.0%
2577
13.7%
4446
10.6%
7393
9.3%
5376
8.9%
0326
7.7%
9284
6.7%
8242
 
5.7%
6205
 
4.9%
ValueCountFrequency (%)
9565
100.0%
ValueCountFrequency (%)
-1348
100.0%
ValueCountFrequency (%)
(1471
100.0%
ValueCountFrequency (%)
)1471
100.0%
ValueCountFrequency (%)
?175
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin60299
76.8%
Common18249
 
23.2%

Most frequent character per script

ValueCountFrequency (%)
e7146
 
11.9%
a6365
 
10.6%
t5555
 
9.2%
r4276
 
7.1%
n4002
 
6.6%
i3425
 
5.7%
l2780
 
4.6%
o2662
 
4.4%
S2615
 
4.3%
A2278
 
3.8%
Other values (36)19195
31.8%
ValueCountFrequency (%)
9565
52.4%
(1471
 
8.1%
)1471
 
8.1%
-1348
 
7.4%
3696
 
3.8%
1674
 
3.7%
2577
 
3.2%
4446
 
2.4%
7393
 
2.2%
5376
 
2.1%
Other values (5)1232
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII78548
100.0%

Most frequent character per block

ValueCountFrequency (%)
9565
 
12.2%
e7146
 
9.1%
a6365
 
8.1%
t5555
 
7.1%
r4276
 
5.4%
n4002
 
5.1%
i3425
 
4.4%
l2780
 
3.5%
o2662
 
3.4%
S2615
 
3.3%
Other values (51)30157
38.4%

Place
Categorical

HIGH CORRELATION

Distinct17
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size151.4 KiB
Commercial area
997 
Residential area
404 
Govt. authority
336 
Bridge / Tunnel
106 
Fuel Station
 
79
Other values (12)
253 

Length

Max length22
Median length15
Mean length14.23034483
Min length4

Characters and Unicode

Total characters30951
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowGovt. authority
2nd rowCommercial area
3rd rowCommercial area
4th rowCommercial area
5th rowOther
ValueCountFrequency (%)
Commercial area997
45.8%
Residential area404
18.6%
Govt. authority336
 
15.4%
Bridge / Tunnel106
 
4.9%
Fuel Station79
 
3.6%
School67
 
3.1%
Hospital64
 
2.9%
Mosque39
 
1.8%
Park20
 
0.9%
One level intersection15
 
0.7%
Other values (7)48
 
2.2%
2021-02-28T18:17:57.838983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
area1422
33.4%
commercial997
23.4%
residential404
 
9.5%
authority336
 
7.9%
govt336
 
7.9%
106
 
2.5%
tunnel106
 
2.5%
bridge106
 
2.5%
fuel79
 
1.9%
station79
 
1.9%
Other values (15)287
 
6.7%

Most occurring characters

ValueCountFrequency (%)
a4778
15.4%
e3653
11.8%
r2928
9.5%
i2420
 
7.8%
2083
 
6.7%
o2027
 
6.5%
m1994
 
6.4%
l1767
 
5.7%
t1690
 
5.5%
c1079
 
3.5%
Other values (28)6532
21.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter26073
84.2%
Uppercase Letter2353
 
7.6%
Space Separator2083
 
6.7%
Other Punctuation442
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
a4778
18.3%
e3653
14.0%
r2928
11.2%
i2420
9.3%
o2027
7.8%
m1994
7.6%
l1767
 
6.8%
t1690
 
6.5%
c1079
 
4.1%
n761
 
2.9%
Other values (12)2976
11.4%
ValueCountFrequency (%)
C1006
42.8%
R404
17.2%
G336
 
14.3%
S147
 
6.2%
B106
 
4.5%
T106
 
4.5%
F79
 
3.4%
H64
 
2.7%
M39
 
1.7%
O28
 
1.2%
Other values (3)38
 
1.6%
ValueCountFrequency (%)
.336
76.0%
/106
 
24.0%
ValueCountFrequency (%)
2083
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin28426
91.8%
Common2525
 
8.2%

Most frequent character per script

ValueCountFrequency (%)
a4778
16.8%
e3653
12.9%
r2928
10.3%
i2420
8.5%
o2027
 
7.1%
m1994
 
7.0%
l1767
 
6.2%
t1690
 
5.9%
c1079
 
3.8%
C1006
 
3.5%
Other values (25)5084
17.9%
ValueCountFrequency (%)
2083
82.5%
.336
 
13.3%
/106
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30951
100.0%

Most frequent character per block

ValueCountFrequency (%)
a4778
15.4%
e3653
11.8%
r2928
9.5%
i2420
 
7.8%
2083
 
6.7%
o2027
 
6.5%
m1994
 
6.4%
l1767
 
5.7%
t1690
 
5.5%
c1079
 
3.5%
Other values (28)6532
21.1%

Location
Categorical

HIGH CORRELATION

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size144.8 KiB
Intersection
1088 
Double road
524 
Highway
213 
Single road
110 
Car parking
 
86
Other values (10)
154 

Length

Max length16
Median length12
Mean length11.11862069
Min length1

Characters and Unicode

Total characters24183
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowIntersection
2nd rowIntersection
3rd rowHighway
4th rowIntersection
5th rowSingle road
ValueCountFrequency (%)
Intersection1088
50.0%
Double road524
24.1%
Highway213
 
9.8%
Single road110
 
5.1%
Car parking86
 
4.0%
Bridge / Tunnel41
 
1.9%
Bridge36
 
1.7%
Two-way road25
 
1.1%
Other(specify)15
 
0.7%
Unknown13
 
0.6%
Other values (5)24
 
1.1%
2021-02-28T18:17:58.232526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
intersection1088
36.1%
road660
21.9%
double524
17.4%
highway213
 
7.1%
single110
 
3.7%
parking95
 
3.2%
car86
 
2.9%
bridge77
 
2.6%
tunnel43
 
1.4%
41
 
1.4%
Other values (7)75
 
2.5%

Most occurring characters

ValueCountFrequency (%)
e2970
12.3%
n2517
10.4%
o2332
 
9.6%
t2203
 
9.1%
r2021
 
8.4%
i1607
 
6.6%
s1103
 
4.6%
c1103
 
4.6%
a1090
 
4.5%
I1088
 
4.5%
Other values (25)6149
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter20992
86.8%
Uppercase Letter2215
 
9.2%
Space Separator880
 
3.6%
Other Punctuation41
 
0.2%
Dash Punctuation25
 
0.1%
Open Punctuation15
 
0.1%
Close Punctuation15
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e2970
14.1%
n2517
12.0%
o2332
11.1%
t2203
10.5%
r2021
9.6%
i1607
7.7%
s1103
 
5.3%
c1103
 
5.3%
a1090
 
5.2%
d757
 
3.6%
Other values (10)3289
15.7%
ValueCountFrequency (%)
I1088
49.1%
D524
23.7%
H213
 
9.6%
S119
 
5.4%
C86
 
3.9%
B77
 
3.5%
T68
 
3.1%
O16
 
0.7%
U13
 
0.6%
R11
 
0.5%
ValueCountFrequency (%)
880
100.0%
ValueCountFrequency (%)
-25
100.0%
ValueCountFrequency (%)
/41
100.0%
ValueCountFrequency (%)
(15
100.0%
ValueCountFrequency (%)
)15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin23207
96.0%
Common976
 
4.0%

Most frequent character per script

ValueCountFrequency (%)
e2970
12.8%
n2517
10.8%
o2332
10.0%
t2203
9.5%
r2021
 
8.7%
i1607
 
6.9%
s1103
 
4.8%
c1103
 
4.8%
a1090
 
4.7%
I1088
 
4.7%
Other values (20)5173
22.3%
ValueCountFrequency (%)
880
90.2%
/41
 
4.2%
-25
 
2.6%
(15
 
1.5%
)15
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII24183
100.0%

Most frequent character per block

ValueCountFrequency (%)
e2970
12.3%
n2517
10.4%
o2332
 
9.6%
t2203
 
9.1%
r2021
 
8.4%
i1607
 
6.6%
s1103
 
4.6%
c1103
 
4.6%
a1090
 
4.5%
I1088
 
4.5%
Other values (25)6149
25.4%

lighting
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size143.5 KiB
day
1265 
night - enough lights
903 
night - weak lights
 
7

Length

Max length21
Median length3
Mean length10.5245977
Min length3

Characters and Unicode

Total characters22891
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownight - enough lights
2nd rownight - enough lights
3rd rownight - enough lights
4th rowday
5th rownight - enough lights
ValueCountFrequency (%)
day1265
58.2%
night - enough lights903
41.5%
night - weak lights7
 
0.3%
2021-02-28T18:17:58.589658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:17:58.709610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
day1265
25.8%
night910
18.6%
lights910
18.6%
910
18.6%
enough903
18.4%
weak7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
2730
11.9%
g2723
11.9%
h2723
11.9%
i1820
 
8.0%
t1820
 
8.0%
n1813
 
7.9%
a1272
 
5.6%
d1265
 
5.5%
y1265
 
5.5%
-910
 
4.0%
Other values (7)4550
19.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter19251
84.1%
Space Separator2730
 
11.9%
Dash Punctuation910
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
g2723
14.1%
h2723
14.1%
i1820
9.5%
t1820
9.5%
n1813
9.4%
a1272
6.6%
d1265
6.6%
y1265
6.6%
e910
 
4.7%
l910
 
4.7%
Other values (5)2730
14.2%
ValueCountFrequency (%)
2730
100.0%
ValueCountFrequency (%)
-910
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin19251
84.1%
Common3640
 
15.9%

Most frequent character per script

ValueCountFrequency (%)
g2723
14.1%
h2723
14.1%
i1820
9.5%
t1820
9.5%
n1813
9.4%
a1272
6.6%
d1265
6.6%
y1265
6.6%
e910
 
4.7%
l910
 
4.7%
Other values (5)2730
14.2%
ValueCountFrequency (%)
2730
75.0%
-910
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII22891
100.0%

Most frequent character per block

ValueCountFrequency (%)
2730
11.9%
g2723
11.9%
h2723
11.9%
i1820
 
8.0%
t1820
 
8.0%
n1813
 
7.9%
a1272
 
5.6%
d1265
 
5.5%
y1265
 
5.5%
-910
 
4.0%
Other values (7)4550
19.9%

Weather
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size131.8 KiB
clear
2149 
rainy
 
11
????
 
7
dusty
 
4
cloudy
 
2
Other values (2)
 
2

Length

Max length7
Median length5
Mean length4.99954023
Min length4

Characters and Unicode

Total characters10874
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowclear
2nd rowclear
3rd rowclear
4th rowclear
5th rowclear
ValueCountFrequency (%)
clear2149
98.8%
rainy11
 
0.5%
????7
 
0.3%
dusty4
 
0.2%
cloudy2
 
0.1%
unknown1
 
< 0.1%
Unknown1
 
< 0.1%
2021-02-28T18:17:59.076380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:17:59.231293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
clear2149
98.8%
rainy11
 
0.5%
7
 
0.3%
dusty4
 
0.2%
cloudy2
 
0.1%
unknown2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a2160
19.9%
r2160
19.9%
c2151
19.8%
l2151
19.8%
e2149
19.8%
?28
 
0.3%
y17
 
0.2%
n17
 
0.2%
i11
 
0.1%
u7
 
0.1%
Other values (7)23
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10845
99.7%
Other Punctuation28
 
0.3%
Uppercase Letter1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a2160
19.9%
r2160
19.9%
c2151
19.8%
l2151
19.8%
e2149
19.8%
y17
 
0.2%
n17
 
0.2%
i11
 
0.1%
u7
 
0.1%
d6
 
0.1%
Other values (5)16
 
0.1%
ValueCountFrequency (%)
U1
100.0%
ValueCountFrequency (%)
?28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10846
99.7%
Common28
 
0.3%

Most frequent character per script

ValueCountFrequency (%)
a2160
19.9%
r2160
19.9%
c2151
19.8%
l2151
19.8%
e2149
19.8%
y17
 
0.2%
n17
 
0.2%
i11
 
0.1%
u7
 
0.1%
d6
 
0.1%
Other values (6)17
 
0.2%
ValueCountFrequency (%)
?28
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10874
100.0%

Most frequent character per block

ValueCountFrequency (%)
a2160
19.9%
r2160
19.9%
c2151
19.8%
l2151
19.8%
e2149
19.8%
?28
 
0.3%
y17
 
0.2%
n17
 
0.2%
i11
 
0.1%
u7
 
0.1%
Other values (7)23
 
0.2%

Road surface
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size127.6 KiB
Dry
2143 
wet
 
22
paved
 
4
????
 
4
Other (specify)
 
1

Length

Max length17
Median length3
Mean length3.017471264
Min length3

Characters and Unicode

Total characters6563
Distinct characters22
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowDry
2nd rowDry
3rd rowDry
4th rowDry
5th rowDry
ValueCountFrequency (%)
Dry2143
98.5%
wet22
 
1.0%
paved4
 
0.2%
????4
 
0.2%
Other (specify)1
 
< 0.1%
covered with sand1
 
< 0.1%
2021-02-28T18:17:59.596100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:17:59.719015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
dry2143
98.4%
wet22
 
1.0%
paved4
 
0.2%
4
 
0.2%
with1
 
< 0.1%
sand1
 
< 0.1%
covered1
 
< 0.1%
specify1
 
< 0.1%
other1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r2145
32.7%
y2144
32.7%
D2143
32.7%
e30
 
0.5%
t24
 
0.4%
w23
 
0.4%
?16
 
0.2%
d6
 
0.1%
p5
 
0.1%
a5
 
0.1%
Other values (12)22
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4398
67.0%
Uppercase Letter2144
32.7%
Other Punctuation16
 
0.2%
Space Separator3
 
< 0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
r2145
48.8%
y2144
48.7%
e30
 
0.7%
t24
 
0.5%
w23
 
0.5%
d6
 
0.1%
p5
 
0.1%
a5
 
0.1%
v5
 
0.1%
h2
 
< 0.1%
Other values (6)9
 
0.2%
ValueCountFrequency (%)
D2143
> 99.9%
O1
 
< 0.1%
ValueCountFrequency (%)
?16
100.0%
ValueCountFrequency (%)
3
100.0%
ValueCountFrequency (%)
(1
100.0%
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6542
99.7%
Common21
 
0.3%

Most frequent character per script

ValueCountFrequency (%)
r2145
32.8%
y2144
32.8%
D2143
32.8%
e30
 
0.5%
t24
 
0.4%
w23
 
0.4%
d6
 
0.1%
p5
 
0.1%
a5
 
0.1%
v5
 
0.1%
Other values (8)12
 
0.2%
ValueCountFrequency (%)
?16
76.2%
3
 
14.3%
(1
 
4.8%
)1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII6563
100.0%

Most frequent character per block

ValueCountFrequency (%)
r2145
32.7%
y2144
32.7%
D2143
32.7%
e30
 
0.5%
t24
 
0.4%
w23
 
0.4%
?16
 
0.2%
d6
 
0.1%
p5
 
0.1%
a5
 
0.1%
Other values (12)22
 
0.3%

Intersection
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size171.1 KiB
Perpendicular Intersection
984 
Not on Intersection
484 
Not on or around 20 meters
353 
Half Perpendicular Intersection
128 
Multiple leg Intersection
 
87
Other values (5)
139 

Length

Max length31
Median length26
Mean length23.48367816
Min length5

Characters and Unicode

Total characters51077
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPerpendicular Intersection
2nd rowPerpendicular Intersection
3rd rowNot on Intersection
4th rowPerpendicular Intersection
5th rowNot on Intersection
ValueCountFrequency (%)
Perpendicular Intersection984
45.2%
Not on Intersection484
22.3%
Not on or around 20 meters353
 
16.2%
Half Perpendicular Intersection128
 
5.9%
Multiple leg Intersection87
 
4.0%
Other53
 
2.4%
Roundabout41
 
1.9%
Turn 38
 
1.7%
????? ??? ?????6
 
0.3%
Deviated intersection1
 
< 0.1%
2021-02-28T18:18:00.094512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:18:00.210433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
intersection1684
26.6%
perpendicular1112
17.6%
not837
13.2%
on837
13.2%
meters353
 
5.6%
20353
 
5.6%
or353
 
5.6%
around353
 
5.6%
half128
 
2.0%
multiple87
 
1.4%
Other values (6)238
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e6527
12.8%
n5749
11.3%
r5058
9.9%
t4740
9.3%
4198
 
8.2%
o4146
 
8.1%
i2885
 
5.6%
c2796
 
5.5%
s2037
 
4.0%
I1683
 
3.3%
Other values (22)11258
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter42115
82.5%
Space Separator4198
 
8.2%
Uppercase Letter3980
 
7.8%
Decimal Number706
 
1.4%
Other Punctuation78
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
e6527
15.5%
n5749
13.7%
r5058
12.0%
t4740
11.3%
o4146
9.8%
i2885
6.9%
c2796
6.6%
s2037
 
4.8%
u1672
 
4.0%
a1635
 
3.9%
Other values (9)4870
11.6%
ValueCountFrequency (%)
I1683
42.3%
P1112
27.9%
N837
21.0%
H128
 
3.2%
M87
 
2.2%
O53
 
1.3%
R41
 
1.0%
T38
 
1.0%
D1
 
< 0.1%
ValueCountFrequency (%)
2353
50.0%
0353
50.0%
ValueCountFrequency (%)
4198
100.0%
ValueCountFrequency (%)
?78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin46095
90.2%
Common4982
 
9.8%

Most frequent character per script

ValueCountFrequency (%)
e6527
14.2%
n5749
12.5%
r5058
11.0%
t4740
10.3%
o4146
9.0%
i2885
 
6.3%
c2796
 
6.1%
s2037
 
4.4%
I1683
 
3.7%
u1672
 
3.6%
Other values (18)8802
19.1%
ValueCountFrequency (%)
4198
84.3%
2353
 
7.1%
0353
 
7.1%
?78
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII51077
100.0%

Most frequent character per block

ValueCountFrequency (%)
e6527
12.8%
n5749
11.3%
r5058
9.9%
t4740
9.3%
4198
 
8.2%
o4146
 
8.1%
i2885
 
5.6%
c2796
 
5.5%
s2037
 
4.0%
I1683
 
3.3%
Other values (22)11258
22.0%

Accident Type
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size163.6 KiB
Perpendicular collision
991 
Person run over
391 
Rear collision
356 
Side collision
150 
Hitting a stationary object on the road
101 
Other values (9)
186 

Length

Max length40
Median length23
Mean length19.96091954
Min length4

Characters and Unicode

Total characters43415
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowPerpendicular collision
2nd rowSide collision
3rd rowHitting a stationary object off the road
4th rowSide collision
5th rowHitting a stationary object on the road
ValueCountFrequency (%)
Perpendicular collision991
45.6%
Person run over391
 
18.0%
Rear collision356
 
16.4%
Side collision150
 
6.9%
Hitting a stationary object on the road101
 
4.6%
deterioration73
 
3.4%
Reciprocal collision41
 
1.9%
Hitting a stationary object off the road30
 
1.4%
Multiple collision27
 
1.2%
Collision while turning6
 
0.3%
Other values (4)9
 
0.4%
2021-02-28T18:18:00.730668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
collision1571
29.4%
perpendicular991
18.6%
person391
 
7.3%
run391
 
7.3%
over391
 
7.3%
rear356
 
6.7%
side150
 
2.8%
object134
 
2.5%
the134
 
2.5%
hitting134
 
2.5%
Other values (14)696
13.0%

Most occurring characters

ValueCountFrequency (%)
i4912
11.3%
o4647
10.7%
l4244
9.8%
r3970
9.1%
n3800
8.8%
e3760
8.7%
3164
 
7.3%
c2773
 
6.4%
s2094
 
4.8%
a1996
 
4.6%
Other values (23)8055
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter38147
87.9%
Space Separator3164
 
7.3%
Uppercase Letter2102
 
4.8%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
i4912
12.9%
o4647
12.2%
l4244
11.1%
r3970
10.4%
n3800
10.0%
e3760
9.9%
c2773
7.3%
s2094
5.5%
a1996
5.2%
u1415
 
3.7%
Other values (12)4536
11.9%
ValueCountFrequency (%)
P1382
65.7%
R397
 
18.9%
S150
 
7.1%
H134
 
6.4%
M27
 
1.3%
C6
 
0.3%
F5
 
0.2%
O1
 
< 0.1%
ValueCountFrequency (%)
3164
100.0%
ValueCountFrequency (%)
(1
100.0%
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin40249
92.7%
Common3166
 
7.3%

Most frequent character per script

ValueCountFrequency (%)
i4912
12.2%
o4647
11.5%
l4244
10.5%
r3970
9.9%
n3800
9.4%
e3760
9.3%
c2773
6.9%
s2094
 
5.2%
a1996
 
5.0%
u1415
 
3.5%
Other values (20)6638
16.5%
ValueCountFrequency (%)
3164
99.9%
(1
 
< 0.1%
)1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII43415
100.0%

Most frequent character per block

ValueCountFrequency (%)
i4912
11.3%
o4647
10.7%
l4244
9.8%
r3970
9.1%
n3800
8.8%
e3760
8.7%
3164
 
7.3%
c2773
 
6.4%
s2094
 
4.8%
a1996
 
4.6%
Other values (23)8055
18.6%

Nationalities
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct64
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size134.5 KiB
UAE
445 
India
325 
Pakistan
205 
Egypt
168 
Bangladesh
158 
Other values (59)
874 

Length

Max length37
Median length5
Mean length6.245057471
Min length3

Characters and Unicode

Total characters13583
Distinct characters47
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)0.7%

Sample

1st rowSyria
2nd rowIndia
3rd rowUAE
4th rowYemen
5th rowUAE
ValueCountFrequency (%)
UAE445
20.5%
India325
14.9%
Pakistan205
9.4%
Egypt168
 
7.7%
Bangladesh158
 
7.3%
Philippines138
 
6.3%
Jordan114
 
5.2%
Syria87
 
4.0%
Palestine73
 
3.4%
Yemen36
 
1.7%
Other values (54)426
19.6%
2021-02-28T18:18:01.293349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
uae445
19.8%
india325
14.4%
pakistan205
9.1%
egypt168
 
7.5%
bangladesh158
 
7.0%
philippines138
 
6.1%
jordan114
 
5.1%
syria87
 
3.9%
palestine73
 
3.2%
yemen36
 
1.6%
Other values (65)503
22.3%

Most occurring characters

ValueCountFrequency (%)
a1835
 
13.5%
i1366
 
10.1%
n1343
 
9.9%
d672
 
4.9%
e654
 
4.8%
E646
 
4.8%
s626
 
4.6%
t594
 
4.4%
p513
 
3.8%
A495
 
3.6%
Other values (37)4839
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10338
76.1%
Uppercase Letter3165
 
23.3%
Space Separator79
 
0.6%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a1835
17.8%
i1366
13.2%
n1343
13.0%
d672
 
6.5%
e654
 
6.3%
s626
 
6.1%
t594
 
5.7%
p513
 
5.0%
l460
 
4.4%
r363
 
3.5%
Other values (14)1912
18.5%
ValueCountFrequency (%)
E646
20.4%
A495
15.6%
U474
15.0%
P417
13.2%
I363
11.5%
S204
 
6.4%
B194
 
6.1%
J116
 
3.7%
L48
 
1.5%
N37
 
1.2%
Other values (11)171
 
5.4%
ValueCountFrequency (%)
79
100.0%
ValueCountFrequency (%)
/1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13503
99.4%
Common80
 
0.6%

Most frequent character per script

ValueCountFrequency (%)
a1835
 
13.6%
i1366
 
10.1%
n1343
 
9.9%
d672
 
5.0%
e654
 
4.8%
E646
 
4.8%
s626
 
4.6%
t594
 
4.4%
p513
 
3.8%
A495
 
3.7%
Other values (35)4759
35.2%
ValueCountFrequency (%)
79
98.8%
/1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII13583
100.0%

Most frequent character per block

ValueCountFrequency (%)
a1835
 
13.5%
i1366
 
10.1%
n1343
 
9.9%
d672
 
4.9%
e654
 
4.8%
E646
 
4.8%
s626
 
4.6%
t594
 
4.4%
p513
 
3.8%
A495
 
3.6%
Other values (37)4839
35.6%

Seat Belt
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
False
1191 
True
984 
ValueCountFrequency (%)
False1191
54.8%
True984
45.2%
2021-02-28T18:18:01.466250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Injured person position
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size137.9 KiB
Driver
957 
Passenger
820 
Pedestrian
398 

Length

Max length10
Median length9
Mean length7.862988506
Min length6

Characters and Unicode

Total characters17102
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPassenger
2nd rowPassenger
3rd rowPassenger
4th rowPassenger
5th rowPassenger
ValueCountFrequency (%)
Driver957
44.0%
Passenger820
37.7%
Pedestrian398
18.3%
2021-02-28T18:18:01.837191image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:18:01.991434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
driver957
44.0%
passenger820
37.7%
pedestrian398
18.3%

Most occurring characters

ValueCountFrequency (%)
e3393
19.8%
r3132
18.3%
s2038
11.9%
i1355
 
7.9%
P1218
 
7.1%
a1218
 
7.1%
n1218
 
7.1%
D957
 
5.6%
v957
 
5.6%
g820
 
4.8%
Other values (2)796
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14927
87.3%
Uppercase Letter2175
 
12.7%

Most frequent character per category

ValueCountFrequency (%)
e3393
22.7%
r3132
21.0%
s2038
13.7%
i1355
 
9.1%
a1218
 
8.2%
n1218
 
8.2%
v957
 
6.4%
g820
 
5.5%
d398
 
2.7%
t398
 
2.7%
ValueCountFrequency (%)
P1218
56.0%
D957
44.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17102
100.0%

Most frequent character per script

ValueCountFrequency (%)
e3393
19.8%
r3132
18.3%
s2038
11.9%
i1355
 
7.9%
P1218
 
7.1%
a1218
 
7.1%
n1218
 
7.1%
D957
 
5.6%
v957
 
5.6%
g820
 
4.8%
Other values (2)796
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII17102
100.0%

Most frequent character per block

ValueCountFrequency (%)
e3393
19.8%
r3132
18.3%
s2038
11.9%
i1355
 
7.9%
P1218
 
7.1%
a1218
 
7.1%
n1218
 
7.1%
D957
 
5.6%
v957
 
5.6%
g820
 
4.8%
Other values (2)796
 
4.7%

Injured person's seat
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size153.5 KiB
Driver's seat
950 
Back passenger seat
487 
No passenger
400 
Front passenger seat
324 
bike passenger
 
14

Length

Max length20
Median length13
Mean length15.20873563
Min length12

Characters and Unicode

Total characters33079
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFront passenger seat
2nd rowFront passenger seat
3rd rowFront passenger seat
4th rowFront passenger seat
5th rowBack passenger seat
ValueCountFrequency (%)
Driver's seat950
43.7%
Back passenger seat487
22.4%
No passenger400
18.4%
Front passenger seat324
 
14.9%
bike passenger14
 
0.6%
2021-02-28T18:18:02.359764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:18:02.489689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
seat1761
34.1%
passenger1225
23.7%
driver's950
18.4%
back487
 
9.4%
no400
 
7.8%
front324
 
6.3%
bike14
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e5175
15.6%
s5161
15.6%
a3473
10.5%
r3449
10.4%
2986
9.0%
t2085
 
6.3%
n1549
 
4.7%
p1225
 
3.7%
g1225
 
3.7%
i964
 
2.9%
Other values (10)5787
17.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter26982
81.6%
Space Separator2986
 
9.0%
Uppercase Letter2161
 
6.5%
Other Punctuation950
 
2.9%

Most frequent character per category

ValueCountFrequency (%)
e5175
19.2%
s5161
19.1%
a3473
12.9%
r3449
12.8%
t2085
7.7%
n1549
 
5.7%
p1225
 
4.5%
g1225
 
4.5%
i964
 
3.6%
v950
 
3.5%
Other values (4)1726
 
6.4%
ValueCountFrequency (%)
D950
44.0%
B487
22.5%
N400
18.5%
F324
 
15.0%
ValueCountFrequency (%)
2986
100.0%
ValueCountFrequency (%)
'950
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin29143
88.1%
Common3936
 
11.9%

Most frequent character per script

ValueCountFrequency (%)
e5175
17.8%
s5161
17.7%
a3473
11.9%
r3449
11.8%
t2085
7.2%
n1549
 
5.3%
p1225
 
4.2%
g1225
 
4.2%
i964
 
3.3%
D950
 
3.3%
Other values (8)3887
13.3%
ValueCountFrequency (%)
2986
75.9%
'950
 
24.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII33079
100.0%

Most frequent character per block

ValueCountFrequency (%)
e5175
15.6%
s5161
15.6%
a3473
10.5%
r3449
10.4%
2986
9.0%
t2085
 
6.3%
n1549
 
4.7%
p1225
 
3.7%
g1225
 
3.7%
i964
 
2.9%
Other values (10)5787
17.5%

Degree of the injury
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size133.9 KiB
Minor
1289 
Moderate
661 
Severe
154 
Death
 
71

Length

Max length8
Median length5
Mean length5.982528736
Min length5

Characters and Unicode

Total characters13012
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMinor
2nd rowMinor
3rd rowSevere
4th rowMinor
5th rowModerate
ValueCountFrequency (%)
Minor1289
59.3%
Moderate661
30.4%
Severe154
 
7.1%
Death71
 
3.3%
2021-02-28T18:18:02.963442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:18:03.121328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
minor1289
59.3%
moderate661
30.4%
severe154
 
7.1%
death71
 
3.3%

Most occurring characters

ValueCountFrequency (%)
r2104
16.2%
M1950
15.0%
o1950
15.0%
e1855
14.3%
i1289
9.9%
n1289
9.9%
a732
 
5.6%
t732
 
5.6%
d661
 
5.1%
S154
 
1.2%
Other values (3)296
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10837
83.3%
Uppercase Letter2175
 
16.7%

Most frequent character per category

ValueCountFrequency (%)
r2104
19.4%
o1950
18.0%
e1855
17.1%
i1289
11.9%
n1289
11.9%
a732
 
6.8%
t732
 
6.8%
d661
 
6.1%
v154
 
1.4%
h71
 
0.7%
ValueCountFrequency (%)
M1950
89.7%
S154
 
7.1%
D71
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Latin13012
100.0%

Most frequent character per script

ValueCountFrequency (%)
r2104
16.2%
M1950
15.0%
o1950
15.0%
e1855
14.3%
i1289
9.9%
n1289
9.9%
a732
 
5.6%
t732
 
5.6%
d661
 
5.1%
S154
 
1.2%
Other values (3)296
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII13012
100.0%

Most frequent character per block

ValueCountFrequency (%)
r2104
16.2%
M1950
15.0%
o1950
15.0%
e1855
14.3%
i1289
9.9%
n1289
9.9%
a732
 
5.6%
t732
 
5.6%
d661
 
5.1%
S154
 
1.2%
Other values (3)296
 
2.3%

Age of the injured
Real number (ℝ≥0)

HIGH CORRELATION

Distinct78
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.30344828
Minimum0
Maximum602
Zeros6
Zeros (%)0.3%
Memory size17.1 KiB
2021-02-28T18:18:03.326214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q124
median31
Q341
95-th percentile58
Maximum602
Range602
Interquartile range (IQR)17

Descriptive statistics

Standard deviation18.79886425
Coefficient of variation (CV)0.581946054
Kurtosis387.0328927
Mean32.30344828
Median Absolute Deviation (MAD)8
Skewness12.88275432
Sum70260
Variance353.3972972
MonotocityNot monotonic
2021-02-28T18:18:03.666857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2790
 
4.1%
2687
 
4.0%
3178
 
3.6%
2976
 
3.5%
2876
 
3.5%
3073
 
3.4%
3373
 
3.4%
2572
 
3.3%
2471
 
3.3%
2365
 
3.0%
Other values (68)1414
65.0%
ValueCountFrequency (%)
06
 
0.3%
116
0.7%
214
0.6%
316
0.7%
417
0.8%
522
1.0%
616
0.7%
712
0.6%
826
1.2%
916
0.7%
ValueCountFrequency (%)
6021
 
< 0.1%
851
 
< 0.1%
751
 
< 0.1%
743
0.1%
731
 
< 0.1%
721
 
< 0.1%
712
 
0.1%
701
 
< 0.1%
695
0.2%
682
 
0.1%

Gender
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size123.3 KiB
M
1475 
F
700 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2175
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowM
3rd rowM
4th rowF
5th rowF
ValueCountFrequency (%)
M1475
67.8%
F700
32.2%
2021-02-28T18:18:04.149881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:18:04.265760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
m1475
67.8%
f700
32.2%

Most occurring characters

ValueCountFrequency (%)
M1475
67.8%
F700
32.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2175
100.0%

Most frequent character per category

ValueCountFrequency (%)
M1475
67.8%
F700
32.2%

Most occurring scripts

ValueCountFrequency (%)
Latin2175
100.0%

Most frequent character per script

ValueCountFrequency (%)
M1475
67.8%
F700
32.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2175
100.0%

Most frequent character per block

ValueCountFrequency (%)
M1475
67.8%
F700
32.2%

Gender of the injured
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size131.1 KiB
Male
1475 
Female
700 

Length

Max length6
Median length4
Mean length4.643678161
Min length4

Characters and Unicode

Total characters10100
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowFemale
ValueCountFrequency (%)
Male1475
67.8%
Female700
32.2%
2021-02-28T18:18:04.657311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:18:05.107051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
male1475
67.8%
female700
32.2%

Most occurring characters

ValueCountFrequency (%)
e2875
28.5%
a2175
21.5%
l2175
21.5%
M1475
14.6%
F700
 
6.9%
m700
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7925
78.5%
Uppercase Letter2175
 
21.5%

Most frequent character per category

ValueCountFrequency (%)
e2875
36.3%
a2175
27.4%
l2175
27.4%
m700
 
8.8%
ValueCountFrequency (%)
M1475
67.8%
F700
32.2%

Most occurring scripts

ValueCountFrequency (%)
Latin10100
100.0%

Most frequent character per script

ValueCountFrequency (%)
e2875
28.5%
a2175
21.5%
l2175
21.5%
M1475
14.6%
F700
 
6.9%
m700
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10100
100.0%

Most frequent character per block

ValueCountFrequency (%)
e2875
28.5%
a2175
21.5%
l2175
21.5%
M1475
14.6%
F700
 
6.9%
m700
 
6.9%

Inp Age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct78
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.30344828
Minimum0
Maximum602
Zeros6
Zeros (%)0.3%
Memory size17.1 KiB
2021-02-28T18:18:05.260966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q124
median31
Q341
95-th percentile58
Maximum602
Range602
Interquartile range (IQR)17

Descriptive statistics

Standard deviation18.79886425
Coefficient of variation (CV)0.581946054
Kurtosis387.0328927
Mean32.30344828
Median Absolute Deviation (MAD)8
Skewness12.88275432
Sum70260
Variance353.3972972
MonotocityNot monotonic
2021-02-28T18:18:05.466847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2790
 
4.1%
2687
 
4.0%
3178
 
3.6%
2976
 
3.5%
2876
 
3.5%
3073
 
3.4%
3373
 
3.4%
2572
 
3.3%
2471
 
3.3%
2365
 
3.0%
Other values (68)1414
65.0%
ValueCountFrequency (%)
06
 
0.3%
116
0.7%
214
0.6%
316
0.7%
417
0.8%
522
1.0%
616
0.7%
712
0.6%
826
1.2%
916
0.7%
ValueCountFrequency (%)
6021
 
< 0.1%
851
 
< 0.1%
751
 
< 0.1%
743
0.1%
731
 
< 0.1%
721
 
< 0.1%
712
 
0.1%
701
 
< 0.1%
695
0.2%
682
 
0.1%

Age Group
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size150.2 KiB
18 to 30 years
809 
31 to 45 years
705 
46 to 60 years
334 
8 to 17 years
146 
1 to 7 years
113 
Other values (2)
 
68

Length

Max length14
Median length14
Mean length13.64689655
Min length8

Characters and Unicode

Total characters29682
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46 to 60 years
2nd row46 to 60 years
3rd row31 to 45 years
4th row31 to 45 years
5th row31 to 45 years
ValueCountFrequency (%)
18 to 30 years809
37.2%
31 to 45 years705
32.4%
46 to 60 years334
15.4%
8 to 17 years146
 
6.7%
1 to 7 years113
 
5.2%
Above 6062
 
2.9%
Undefined6
 
0.3%
2021-02-28T18:18:05.936580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:18:06.050132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
to2107
24.6%
years2107
24.6%
30809
 
9.5%
18809
 
9.5%
31705
 
8.2%
45705
 
8.2%
60396
 
4.6%
46334
 
3.9%
8146
 
1.7%
17146
 
1.7%
Other values (4)294
 
3.4%

Most occurring characters

ValueCountFrequency (%)
6389
21.5%
e2181
 
7.3%
o2169
 
7.3%
t2107
 
7.1%
y2107
 
7.1%
a2107
 
7.1%
r2107
 
7.1%
s2107
 
7.1%
11773
 
6.0%
31514
 
5.1%
Other values (14)5121
17.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15045
50.7%
Decimal Number8180
27.6%
Space Separator6389
21.5%
Uppercase Letter68
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
e2181
14.5%
o2169
14.4%
t2107
14.0%
y2107
14.0%
a2107
14.0%
r2107
14.0%
s2107
14.0%
b62
 
0.4%
v62
 
0.4%
n12
 
0.1%
Other values (3)24
 
0.2%
ValueCountFrequency (%)
11773
21.7%
31514
18.5%
01205
14.7%
41039
12.7%
8955
11.7%
6730
8.9%
5705
 
8.6%
7259
 
3.2%
ValueCountFrequency (%)
A62
91.2%
U6
 
8.8%
ValueCountFrequency (%)
6389
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15113
50.9%
Common14569
49.1%

Most frequent character per script

ValueCountFrequency (%)
e2181
14.4%
o2169
14.4%
t2107
13.9%
y2107
13.9%
a2107
13.9%
r2107
13.9%
s2107
13.9%
A62
 
0.4%
b62
 
0.4%
v62
 
0.4%
Other values (5)42
 
0.3%
ValueCountFrequency (%)
6389
43.9%
11773
 
12.2%
31514
 
10.4%
01205
 
8.3%
41039
 
7.1%
8955
 
6.6%
6730
 
5.0%
5705
 
4.8%
7259
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII29682
100.0%

Most frequent character per block

ValueCountFrequency (%)
6389
21.5%
e2181
 
7.3%
o2169
 
7.3%
t2107
 
7.1%
y2107
 
7.1%
a2107
 
7.1%
r2107
 
7.1%
s2107
 
7.1%
11773
 
6.0%
31514
 
5.1%
Other values (14)5121
17.3%

Date - Month
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size127.6 KiB
Mar
264 
Jan
236 
Apr
209 
May
200 
Feb
190 
Other values (7)
1076 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6525
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJan
2nd rowJan
3rd rowJan
4th rowJan
5th rowJan
ValueCountFrequency (%)
Mar264
12.1%
Jan236
10.9%
Apr209
9.6%
May200
9.2%
Feb190
8.7%
Jun188
8.6%
Aug158
7.3%
Dec154
7.1%
Sep153
7.0%
Oct149
6.9%
Other values (2)274
12.6%
2021-02-28T18:18:06.433661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mar264
12.1%
jan236
10.9%
apr209
9.6%
may200
9.2%
feb190
8.7%
jun188
8.6%
aug158
7.3%
dec154
7.1%
sep153
7.0%
oct149
6.9%
Other values (2)274
12.6%

Most occurring characters

ValueCountFrequency (%)
a700
 
10.7%
J549
 
8.4%
e497
 
7.6%
r473
 
7.2%
u471
 
7.2%
M464
 
7.1%
n424
 
6.5%
A367
 
5.6%
p362
 
5.5%
c303
 
4.6%
Other values (12)1915
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4350
66.7%
Uppercase Letter2175
33.3%

Most frequent character per category

ValueCountFrequency (%)
a700
16.1%
e497
11.4%
r473
10.9%
u471
10.8%
n424
9.7%
p362
8.3%
c303
7.0%
y200
 
4.6%
b190
 
4.4%
g158
 
3.6%
Other values (4)572
13.1%
ValueCountFrequency (%)
J549
25.2%
M464
21.3%
A367
16.9%
F190
 
8.7%
D154
 
7.1%
S153
 
7.0%
O149
 
6.9%
N149
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Latin6525
100.0%

Most frequent character per script

ValueCountFrequency (%)
a700
 
10.7%
J549
 
8.4%
e497
 
7.6%
r473
 
7.2%
u471
 
7.2%
M464
 
7.1%
n424
 
6.5%
A367
 
5.6%
p362
 
5.5%
c303
 
4.6%
Other values (12)1915
29.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII6525
100.0%

Most frequent character per block

ValueCountFrequency (%)
a700
 
10.7%
J549
 
8.4%
e497
 
7.6%
r473
 
7.2%
u471
 
7.2%
M464
 
7.1%
n424
 
6.5%
A367
 
5.6%
p362
 
5.5%
c303
 
4.6%
Other values (12)1915
29.3%

Nationality of the Injured person
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size140.2 KiB
Asian States
877 
Arab States
578 
UAE
445 
Other
210 
GCC
 
65

Length

Max length12
Median length11
Mean length8.948045977
Min length3

Characters and Unicode

Total characters19462
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArab States
2nd rowAsian States
3rd rowUAE
4th rowGCC
5th rowUAE
ValueCountFrequency (%)
Asian States877
40.3%
Arab States578
26.6%
UAE445
20.5%
Other210
 
9.7%
GCC65
 
3.0%
2021-02-28T18:18:06.794455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:18:06.907412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
states1455
40.1%
asian877
24.2%
arab578
 
15.9%
uae445
 
12.3%
other210
 
5.8%
gcc65
 
1.8%

Most occurring characters

ValueCountFrequency (%)
t3120
16.0%
a2910
15.0%
s2332
12.0%
A1900
9.8%
e1665
8.6%
1455
7.5%
S1455
7.5%
i877
 
4.5%
n877
 
4.5%
r788
 
4.0%
Other values (7)2083
10.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13357
68.6%
Uppercase Letter4650
 
23.9%
Space Separator1455
 
7.5%

Most frequent character per category

ValueCountFrequency (%)
t3120
23.4%
a2910
21.8%
s2332
17.5%
e1665
12.5%
i877
 
6.6%
n877
 
6.6%
r788
 
5.9%
b578
 
4.3%
h210
 
1.6%
ValueCountFrequency (%)
A1900
40.9%
S1455
31.3%
U445
 
9.6%
E445
 
9.6%
O210
 
4.5%
C130
 
2.8%
G65
 
1.4%
ValueCountFrequency (%)
1455
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18007
92.5%
Common1455
 
7.5%

Most frequent character per script

ValueCountFrequency (%)
t3120
17.3%
a2910
16.2%
s2332
13.0%
A1900
10.6%
e1665
9.2%
S1455
8.1%
i877
 
4.9%
n877
 
4.9%
r788
 
4.4%
b578
 
3.2%
Other values (6)1505
8.4%
ValueCountFrequency (%)
1455
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII19462
100.0%

Most frequent character per block

ValueCountFrequency (%)
t3120
16.0%
a2910
15.0%
s2332
12.0%
A1900
9.8%
e1665
8.6%
1455
7.5%
S1455
7.5%
i877
 
4.5%
n877
 
4.5%
r788
 
4.0%
Other values (7)2083
10.7%

Day.1
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size136.3 KiB
Sunday
333 
Monday
331 
Thursday
323 
Saturday
320 
Friday
310 
Other values (2)
558 

Length

Max length9
Median length7
Mean length7.123678161
Min length6

Characters and Unicode

Total characters15494
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSaturday
2nd rowSaturday
3rd rowSaturday
4th rowSaturday
5th rowSaturday
ValueCountFrequency (%)
Sunday333
15.3%
Monday331
15.2%
Thursday323
14.9%
Saturday320
14.7%
Friday310
14.3%
Wednesday300
13.8%
Tuesday258
11.9%
2021-02-28T18:18:07.278179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:18:07.404128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
sunday333
15.3%
monday331
15.2%
thursday323
14.9%
saturday320
14.7%
friday310
14.3%
wednesday300
13.8%
tuesday258
11.9%

Most occurring characters

ValueCountFrequency (%)
a2495
16.1%
d2475
16.0%
y2175
14.0%
u1234
8.0%
n964
 
6.2%
r953
 
6.2%
s881
 
5.7%
e858
 
5.5%
S653
 
4.2%
T581
 
3.7%
Other values (7)2225
14.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13319
86.0%
Uppercase Letter2175
 
14.0%

Most frequent character per category

ValueCountFrequency (%)
a2495
18.7%
d2475
18.6%
y2175
16.3%
u1234
9.3%
n964
 
7.2%
r953
 
7.2%
s881
 
6.6%
e858
 
6.4%
o331
 
2.5%
h323
 
2.4%
Other values (2)630
 
4.7%
ValueCountFrequency (%)
S653
30.0%
T581
26.7%
M331
15.2%
F310
14.3%
W300
13.8%

Most occurring scripts

ValueCountFrequency (%)
Latin15494
100.0%

Most frequent character per script

ValueCountFrequency (%)
a2495
16.1%
d2475
16.0%
y2175
14.0%
u1234
8.0%
n964
 
6.2%
r953
 
6.2%
s881
 
5.7%
e858
 
5.5%
S653
 
4.2%
T581
 
3.7%
Other values (7)2225
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII15494
100.0%

Most frequent character per block

ValueCountFrequency (%)
a2495
16.1%
d2475
16.0%
y2175
14.0%
u1234
8.0%
n964
 
6.2%
r953
 
6.2%
s881
 
5.7%
e858
 
5.5%
S653
 
4.2%
T581
 
3.7%
Other values (7)2225
14.4%

Iac Rep Time
Categorical

HIGH CARDINALITY

Distinct535
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Memory size131.1 KiB
15:00
 
41
5:30
 
23
7:00
 
22
11:00
 
20
17:30
 
20
Other values (530)
2049 

Length

Max length5
Median length5
Mean length4.662068966
Min length4

Characters and Unicode

Total characters10140
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique187 ?
Unique (%)8.6%

Sample

1st row0:00
2nd row0:35
3rd row4:20
4th row16:19
5th row22:27
ValueCountFrequency (%)
15:0041
 
1.9%
5:3023
 
1.1%
7:0022
 
1.0%
11:0020
 
0.9%
17:3020
 
0.9%
16:3020
 
0.9%
22:0019
 
0.9%
21:3019
 
0.9%
22:1518
 
0.8%
19:3018
 
0.8%
Other values (525)1955
89.9%
2021-02-28T18:18:07.891708image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
15:0041
 
1.9%
5:3023
 
1.1%
7:0022
 
1.0%
11:0020
 
0.9%
17:3020
 
0.9%
16:3020
 
0.9%
22:0019
 
0.9%
21:3019
 
0.9%
22:1518
 
0.8%
19:3018
 
0.8%
Other values (525)1955
89.9%

Most occurring characters

ValueCountFrequency (%)
:2175
21.4%
01807
17.8%
11745
17.2%
21089
10.7%
51055
10.4%
3749
 
7.4%
4498
 
4.9%
7278
 
2.7%
8263
 
2.6%
9252
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7965
78.6%
Other Punctuation2175
 
21.4%

Most frequent character per category

ValueCountFrequency (%)
01807
22.7%
11745
21.9%
21089
13.7%
51055
13.2%
3749
9.4%
4498
 
6.3%
7278
 
3.5%
8263
 
3.3%
9252
 
3.2%
6229
 
2.9%
ValueCountFrequency (%)
:2175
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10140
100.0%

Most frequent character per script

ValueCountFrequency (%)
:2175
21.4%
01807
17.8%
11745
17.2%
21089
10.7%
51055
10.4%
3749
 
7.4%
4498
 
4.9%
7278
 
2.7%
8263
 
2.6%
9252
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII10140
100.0%

Most frequent character per block

ValueCountFrequency (%)
:2175
21.4%
01807
17.8%
11745
17.2%
21089
10.7%
51055
10.4%
3749
 
7.4%
4498
 
4.9%
7278
 
2.7%
8263
 
2.6%
9252
 
2.5%

Time
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size125.4 KiB
PM
1639 
AM
536 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4350
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPM
2nd rowPM
3rd rowPM
4th rowPM
5th rowPM
ValueCountFrequency (%)
PM1639
75.4%
AM536
 
24.6%
2021-02-28T18:18:08.198723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:18:08.298982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
pm1639
75.4%
am536
 
24.6%

Most occurring characters

ValueCountFrequency (%)
M2175
50.0%
P1639
37.7%
A536
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4350
100.0%

Most frequent character per category

ValueCountFrequency (%)
M2175
50.0%
P1639
37.7%
A536
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Latin4350
100.0%

Most frequent character per script

ValueCountFrequency (%)
M2175
50.0%
P1639
37.7%
A536
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4350
100.0%

Most frequent character per block

ValueCountFrequency (%)
M2175
50.0%
P1639
37.7%
A536
 
12.3%

Age Group - Ministry
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size150.5 KiB
18 to 30 years
809 
31 to 45 years
705 
46 to 60 years
334 
Below 18 years
259 
Above 60
 
62

Length

Max length14
Median length14
Mean length13.81517241
Min length8

Characters and Unicode

Total characters30048
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46 to 60 years
2nd row46 to 60 years
3rd row31 to 45 years
4th row31 to 45 years
5th row31 to 45 years
ValueCountFrequency (%)
18 to 30 years809
37.2%
31 to 45 years705
32.4%
46 to 60 years334
15.4%
Below 18 years259
 
11.9%
Above 6062
 
2.9%
Undefined6
 
0.3%
2021-02-28T18:18:08.654809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:18:08.857691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
years2107
25.4%
to1848
22.3%
181068
12.9%
30809
 
9.7%
31705
 
8.5%
45705
 
8.5%
60396
 
4.8%
46334
 
4.0%
below259
 
3.1%
above62
 
0.7%

Most occurring characters

ValueCountFrequency (%)
6124
20.4%
e2440
 
8.1%
o2169
 
7.2%
y2107
 
7.0%
a2107
 
7.0%
r2107
 
7.0%
s2107
 
7.0%
t1848
 
6.2%
11773
 
5.9%
31514
 
5.0%
Other values (16)5752
19.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15563
51.8%
Decimal Number8034
26.7%
Space Separator6124
 
20.4%
Uppercase Letter327
 
1.1%

Most frequent character per category

ValueCountFrequency (%)
e2440
15.7%
o2169
13.9%
y2107
13.5%
a2107
13.5%
r2107
13.5%
s2107
13.5%
t1848
11.9%
l259
 
1.7%
w259
 
1.7%
b62
 
0.4%
Other values (5)98
 
0.6%
ValueCountFrequency (%)
11773
22.1%
31514
18.8%
01205
15.0%
81068
13.3%
41039
12.9%
6730
9.1%
5705
 
8.8%
ValueCountFrequency (%)
B259
79.2%
A62
 
19.0%
U6
 
1.8%
ValueCountFrequency (%)
6124
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15890
52.9%
Common14158
47.1%

Most frequent character per script

ValueCountFrequency (%)
e2440
15.4%
o2169
13.7%
y2107
13.3%
a2107
13.3%
r2107
13.3%
s2107
13.3%
t1848
11.6%
B259
 
1.6%
l259
 
1.6%
w259
 
1.6%
Other values (8)228
 
1.4%
ValueCountFrequency (%)
6124
43.3%
11773
 
12.5%
31514
 
10.7%
01205
 
8.5%
81068
 
7.5%
41039
 
7.3%
6730
 
5.2%
5705
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30048
100.0%

Most frequent character per block

ValueCountFrequency (%)
6124
20.4%
e2440
 
8.1%
o2169
 
7.2%
y2107
 
7.0%
a2107
 
7.0%
r2107
 
7.0%
s2107
 
7.0%
t1848
 
6.2%
11773
 
5.9%
31514
 
5.0%
Other values (16)5752
19.1%

Computed2
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.3 KiB
1
2175 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2175
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
12175
100.0%
2021-02-28T18:18:09.262889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:18:09.504242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
12175
100.0%

Most occurring characters

ValueCountFrequency (%)
12175
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2175
100.0%

Most frequent character per category

ValueCountFrequency (%)
12175
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2175
100.0%

Most frequent character per script

ValueCountFrequency (%)
12175
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2175
100.0%

Most frequent character per block

ValueCountFrequency (%)
12175
100.0%

Area
Categorical

HIGH CORRELATION

Distinct39
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size144.9 KiB
Mushrif
307 
Markaziya
223 
Hadbat Al Zafaran
219 
Al Butain
157 
Madinat Zayed (Abu Dhabi)
149 
Other values (34)
1120 

Length

Max length29
Median length9
Mean length11.18344828
Min length4

Characters and Unicode

Total characters24324
Distinct characters47
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.2%

Sample

1st rowMushrif
2nd rowAl Wahda
3rd rowMina
4th rowMushrif
5th rowMushrif
ValueCountFrequency (%)
Mushrif307
14.1%
Markaziya223
 
10.3%
Hadbat Al Zafaran219
 
10.1%
Al Butain157
 
7.2%
Madinat Zayed (Abu Dhabi)149
 
6.9%
Mina126
 
5.8%
Khaldiya125
 
5.7%
Rawdah100
 
4.6%
Dhafra (Abu Dhabi)94
 
4.3%
Al Wahda92
 
4.2%
Other values (29)583
26.8%
2021-02-28T18:18:10.063747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
al656
16.1%
markaziya310
 
7.6%
mushrif307
 
7.6%
abu243
 
6.0%
dhabi243
 
6.0%
zafaran219
 
5.4%
hadbat219
 
5.4%
butain157
 
3.9%
zayed154
 
3.8%
madinat149
 
3.7%
Other values (41)1406
34.6%

Most occurring characters

ValueCountFrequency (%)
a4572
18.8%
1888
 
7.8%
i1745
 
7.2%
r1479
 
6.1%
h1226
 
5.0%
d1043
 
4.3%
A981
 
4.0%
M954
 
3.9%
t858
 
3.5%
l843
 
3.5%
Other values (37)8735
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter17715
72.8%
Uppercase Letter4062
 
16.7%
Space Separator1888
 
7.8%
Open Punctuation328
 
1.3%
Close Punctuation328
 
1.3%
Other Punctuation3
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a4572
25.8%
i1745
 
9.9%
r1479
 
8.3%
h1226
 
6.9%
d1043
 
5.9%
t858
 
4.8%
l843
 
4.8%
n829
 
4.7%
b820
 
4.6%
f738
 
4.2%
Other values (14)3562
20.1%
ValueCountFrequency (%)
A981
24.2%
M954
23.5%
Z373
 
9.2%
D337
 
8.3%
H268
 
6.6%
B268
 
6.6%
K188
 
4.6%
R119
 
2.9%
S99
 
2.4%
G93
 
2.3%
Other values (9)382
 
9.4%
ValueCountFrequency (%)
1888
100.0%
ValueCountFrequency (%)
(328
100.0%
ValueCountFrequency (%)
)328
100.0%
ValueCountFrequency (%)
'3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin21777
89.5%
Common2547
 
10.5%

Most frequent character per script

ValueCountFrequency (%)
a4572
21.0%
i1745
 
8.0%
r1479
 
6.8%
h1226
 
5.6%
d1043
 
4.8%
A981
 
4.5%
M954
 
4.4%
t858
 
3.9%
l843
 
3.9%
n829
 
3.8%
Other values (33)7247
33.3%
ValueCountFrequency (%)
1888
74.1%
(328
 
12.9%
)328
 
12.9%
'3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII24324
100.0%

Most frequent character per block

ValueCountFrequency (%)
a4572
18.8%
1888
 
7.8%
i1745
 
7.2%
r1479
 
6.1%
h1226
 
5.0%
d1043
 
4.3%
A981
 
4.0%
M954
 
3.9%
t858
 
3.5%
l843
 
3.5%
Other values (37)8735
35.9%

Block
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct94
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size142.9 KiB
Not Identified
 
100
Block 54-1
 
90
Block 73-2
 
70
Block 53-1
 
62
Block 40-1
 
59
Other values (89)
1794 

Length

Max length19
Median length10
Mean length10.23402299
Min length10

Characters and Unicode

Total characters22259
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowBlock 23-2
2nd rowBlock 17-1
3rd rowBlock 73-1
4th rowBlock 24-2
5th rowBlock 38-2
ValueCountFrequency (%)
Not Identified100
 
4.6%
Block 54-190
 
4.1%
Block 73-270
 
3.2%
Block 53-162
 
2.9%
Block 40-159
 
2.7%
Block 53-255
 
2.5%
Block 17-154
 
2.5%
Block 27-152
 
2.4%
Block 74-251
 
2.3%
Block 51-150
 
2.3%
Other values (84)1532
70.4%
2021-02-28T18:18:10.533641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
block2049
47.1%
identified125
 
2.9%
not125
 
2.9%
54-190
 
2.1%
73-270
 
1.6%
53-162
 
1.4%
40-159
 
1.4%
53-255
 
1.3%
17-154
 
1.2%
27-152
 
1.2%
Other values (87)1610
37.0%

Most occurring characters

ValueCountFrequency (%)
2176
9.8%
o2174
9.8%
k2050
9.2%
B2049
9.2%
l2049
9.2%
c2049
9.2%
-2049
9.2%
11630
7.3%
21526
6.9%
5720
 
3.2%
Other values (21)3787
17.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9610
43.2%
Decimal Number6147
27.6%
Uppercase Letter2277
 
10.2%
Space Separator2176
 
9.8%
Dash Punctuation2049
 
9.2%

Most frequent character per category

ValueCountFrequency (%)
o2174
22.6%
k2050
21.3%
l2049
21.3%
c2049
21.3%
i276
 
2.9%
e254
 
2.6%
t252
 
2.6%
d251
 
2.6%
n125
 
1.3%
f125
 
1.3%
Other values (4)5
 
0.1%
ValueCountFrequency (%)
11630
26.5%
21526
24.8%
5720
11.7%
3596
 
9.7%
7526
 
8.6%
4500
 
8.1%
9256
 
4.2%
6153
 
2.5%
8123
 
2.0%
0117
 
1.9%
ValueCountFrequency (%)
B2049
90.0%
N125
 
5.5%
I100
 
4.4%
S2
 
0.1%
Z1
 
< 0.1%
ValueCountFrequency (%)
2176
100.0%
ValueCountFrequency (%)
-2049
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11887
53.4%
Common10372
46.6%

Most frequent character per script

ValueCountFrequency (%)
o2174
18.3%
k2050
17.2%
B2049
17.2%
l2049
17.2%
c2049
17.2%
i276
 
2.3%
e254
 
2.1%
t252
 
2.1%
d251
 
2.1%
N125
 
1.1%
Other values (9)358
 
3.0%
ValueCountFrequency (%)
2176
21.0%
-2049
19.8%
11630
15.7%
21526
14.7%
5720
 
6.9%
3596
 
5.7%
7526
 
5.1%
4500
 
4.8%
9256
 
2.5%
6153
 
1.5%
Other values (2)240
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII22259
100.0%

Most frequent character per block

ValueCountFrequency (%)
2176
9.8%
o2174
9.8%
k2050
9.2%
B2049
9.2%
l2049
9.2%
c2049
9.2%
-2049
9.2%
11630
7.3%
21526
6.9%
5720
 
3.2%
Other values (21)3787
17.0%

Str Code
Real number (ℝ≥0)

Distinct129
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.87356322
Minimum0
Maximum935
Zeros8
Zeros (%)0.4%
Memory size17.1 KiB
2021-02-28T18:18:10.766508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q116
median90
Q3150
95-th percentile187
Maximum935
Range935
Interquartile range (IQR)134

Descriptive statistics

Standard deviation72.91067183
Coefficient of variation (CV)0.8590504401
Kurtosis16.90893203
Mean84.87356322
Median Absolute Deviation (MAD)71
Skewness1.855592174
Sum184600
Variance5315.966067
MonotocityNot monotonic
2021-02-28T18:18:11.040352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13157
 
7.2%
16144
 
6.6%
14137
 
6.3%
1294
 
4.3%
2965
 
3.0%
2563
 
2.9%
2450
 
2.3%
17450
 
2.3%
18645
 
2.1%
2645
 
2.1%
Other values (119)1325
60.9%
ValueCountFrequency (%)
08
 
0.4%
1294
4.3%
13157
7.2%
14137
6.3%
1520
 
0.9%
16144
6.6%
1723
 
1.1%
1822
 
1.0%
1931
 
1.4%
2014
 
0.6%
ValueCountFrequency (%)
9352
 
0.1%
6671
 
< 0.1%
1971
 
< 0.1%
1941
 
< 0.1%
1931
 
< 0.1%
1913
 
0.1%
1908
 
0.4%
18931
1.4%
18837
1.7%
18726
1.2%

Accident Description
Categorical

HIGH CARDINALITY
UNIFORM

Distinct2061
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
After examining and inspecting the accident's site, the following was revealed:- the vehicles were travelling from the west to the east taking the second lane from the right. The vehicle that caused the accident didn’t keep a distance from the front vehicle and collided from the front side with the back side of the first afflicted vehicle. Leading the latter to collide from the front side with back side of the second afflicted vehicle and then leading the second to collide from the front side with back side of the third afflicted vehicle. as a result, the passenger of the former vehicle was injured and material damagers were caused to the vehicles. As shown in the diagram.
 
10
After examining and inspecting the accident's site, the following was revealed:- The vehicle that caused the accident was travelling from south to the north taking the right lane. The other afflicted vehicles were travelling to the same direction while taking different lanes. When the afflicted vehicles reach the crossroads they stopped waiting in the traffic. The former violated the traffic rules and was speeding when he crashed into the side-walk and the wall then drifted and collided with the back side of the first afflicted vehicle and as response it crashed with the back side of the second afflicted then sequentially from the sides and the front colliding with the next vehicle then the one after. as a result, ten people were injured from amongst drivers and passengers. They all received medical treatment and two of them were severely injured and were to admitted to the hospital. Material damages were caused to the vehicles and the state's properties. As shown in the diagram.
 
10
After examining and inspecting the accident's site, the following was revealed: the first vehicle that caused the accident was travelling from east to the west taking the second lane from the right. The second, the third and the forth were travelling to the same direction. The first vehicle suddenly stopped beside the Exhibitions Ground. Because The second vehicle was behind the first and wasn’t leave a space, it collided from the front side with the back side of the first vehicle. Then the second collided with the third. then sequentially, the third with the forth. Leading the third to drift to left and to collided from the left side with the front side of the afflicted vehicle(truck). The afflicted vehicle kept on going till it collided from the left side with the right side of the first vehicle, leading the latter to drift and roll-over in the middle of the road. The afflicted vehicle kept on going foreword and met the the forth vehicle halfway and collided from right side with left side of the forth. And they all stopped in the right side lane. as a result, the drivers of the first, second and third vehicles and the passengers were injured. As shown in the diagram.
 
8
After examining and inspecting the accident's site, the following was revealed:- The vehicle that caused the accident was travelling from the west to the east, driving to the middle lane and running the red light . while taking a left turn to the cross-road, the right front side of the vehicle collided with the front side of the first afflicted vehicle, which was travelling from the east to the west taking the right lane, Causing the former vehicle to to spin to the left side and collide from the front side with the second afflicted vehicle which was waiting for the green light on the right lane. resulting in the death of the passenger in the vehicle that caused the accident, severe and moderate injuries and material damages in all the vehicles. The Accident was diagrammed.
 
8
After examining and inspecting the accident's site, the following was revealed:- The vehicle that caused the accident was travelling from the west to the north in the crossroads taking the left lane. The afflicted vehicle was travelling from the east to the west taking the middle lane. The former ran the red light and collided from the right side with the front side of the afflicted vehicle. As shown in the diagram.
 
6
Other values (2056)
2133 

Length

Max length1646
Median length609
Mean length634.8937931
Min length86

Characters and Unicode

Total characters1380894
Distinct characters82
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2011 ?
Unique (%)92.5%

Sample

1st row3803.                   After detection and inspecting the accident site, the following accident chart became clear: While the causing vehicle was coming from the west towards the east and it would like to turn left, the lane is on the far left, the damaged vehicle is coming from the east towards the west and the lane is on the far right. In the case of moving forward, and because of bypassing the red light and driving drunk the causing vehicle was collided from the front right corner. The damaged vehicle is on the right side of the causing vehicle. The accident resulted in a female passenger being injured in the damaged vehicle, as well as material damage to both vehicles. The detection and necessary measures were taken.
2nd row3804.                   After detection and inspection by the accident chart, the following is revealed: -The causing vehicle was coming from the west towards the east with the left lane line, the damaged vehicle was coming from the south towards the north with the right lane line, while the two vehicles were passing and because of bypassing the red traffic light by the offending driver hit the front of the damaged vehicle on the right side of the damaged vehicle, and accordingly the accident was charted.
3rd row3805.                   After detection and inspecting the accident site, the following accident chart became clear: The causing vehicle was coming from the south to the north, and due to the driving in an abnormal state (drunkenness) by the offending driver, the vehicle deviated from the road and hit the front of the causing vehicle with a billboard column. The accident was charted and necessary action was taken.
4th row3806.                   After detection and inspecting the accident site, it became clear that the following accident chart: - The causing vehicle is coming from the south to the north, heading to the west, the path is passed by the arrow, and the damaged vehicle is coming from the north to the south, on the right path. To bypass the red light by the causing vehicle, the front of the damaged vehicle collided with the right side of the causing vehicle. The accident was charted and the crisis was conducted.
5th row3807.                   After detection and inspecting the accident site, the following accident chart became clear: During the rotation of the causing vehicle from the filter triangle, the driver of the causing vehicle could not control the causing vehicle, which led to hitting the front of the causing vehicle on the sidewalk leading to its rollover on its left side. The accident was charted and necessary action was taken.
ValueCountFrequency (%)
After examining and inspecting the accident's site, the following was revealed:- the vehicles were travelling from the west to the east taking the second lane from the right. The vehicle that caused the accident didn’t keep a distance from the front vehicle and collided from the front side with the back side of the first afflicted vehicle. Leading the latter to collide from the front side with back side of the second afflicted vehicle and then leading the second to collide from the front side with back side of the third afflicted vehicle. as a result, the passenger of the former vehicle was injured and material damagers were caused to the vehicles. As shown in the diagram.10
 
0.5%
After examining and inspecting the accident's site, the following was revealed:- The vehicle that caused the accident was travelling from south to the north taking the right lane. The other afflicted vehicles were travelling to the same direction while taking different lanes. When the afflicted vehicles reach the crossroads they stopped waiting in the traffic. The former violated the traffic rules and was speeding when he crashed into the side-walk and the wall then drifted and collided with the back side of the first afflicted vehicle and as response it crashed with the back side of the second afflicted then sequentially from the sides and the front colliding with the next vehicle then the one after. as a result, ten people were injured from amongst drivers and passengers. They all received medical treatment and two of them were severely injured and were to admitted to the hospital. Material damages were caused to the vehicles and the state's properties. As shown in the diagram. 10
 
0.5%
After examining and inspecting the accident's site, the following was revealed: the first vehicle that caused the accident was travelling from east to the west taking the second lane from the right. The second, the third and the forth were travelling to the same direction. The first vehicle suddenly stopped beside the Exhibitions Ground. Because The second vehicle was behind the first and wasn’t leave a space, it collided from the front side with the back side of the first vehicle. Then the second collided with the third. then sequentially, the third with the forth. Leading the third to drift to left and to collided from the left side with the front side of the afflicted vehicle(truck). The afflicted vehicle kept on going till it collided from the left side with the right side of the first vehicle, leading the latter to drift and roll-over in the middle of the road. The afflicted vehicle kept on going foreword and met the the forth vehicle halfway and collided from right side with left side of the forth. And they all stopped in the right side lane. as a result, the drivers of the first, second and third vehicles and the passengers were injured. As shown in the diagram.8
 
0.4%
After examining and inspecting the accident's site, the following was revealed:- The vehicle that caused the accident was travelling from the west to the east, driving to the middle lane and running the red light . while taking a left turn to the cross-road, the right front side of the vehicle collided with the front side of the first afflicted vehicle, which was travelling from the east to the west taking the right lane, Causing the former vehicle to to spin to the left side and collide from the front side with the second afflicted vehicle which was waiting for the green light on the right lane. resulting in the death of the passenger in the vehicle that caused the accident, severe and moderate injuries and material damages in all the vehicles. The Accident was diagrammed.8
 
0.4%
After examining and inspecting the accident's site, the following was revealed:- The vehicle that caused the accident was travelling from the west to the north in the crossroads taking the left lane. The afflicted vehicle was travelling from the east to the west taking the middle lane. The former ran the red light and collided from the right side with the front side of the afflicted vehicle. As shown in the diagram.6
 
0.3%
the vehicle that caused the accident was travelling from the east to the west taking the second lane from the left. The afflicted vehicle was travelling from the east to the west taking the the second lane from the right. The former ran the red light and collided from the right side with the front side of the afflicted vehicle. As a result, the driver and the passengers of the former vehicle, the passengers of the afflicted vehicle were injured and material damages were caused to vehicles. As shown in the digram.5
 
0.2%
After examining and inspecting the accident's site, the following was revealed:- the vehicle that caused the accident was travelling from the north to the south making a turn to the west. The driver was driving in an unstable state (diabetic). He crashed into the pavement and over the grass. Then he kept on driving till he reach the main road and crossed it, after that, crashed again from the front into the pavement of the island leading the vehicle to roll-over and land there. As shown in the diagram.4
 
0.2%
After examining and inspecting the accident's site, the following was revealed: the vehicle that caused the accident was travelling from west to the east taking the the second lane from the left. The afflicted vehicle was travelling from the north to te south taking the middle lane. When the former reach the crossroads and ran the red light, he collided from from the left side with the front side of the afflicted vehicle. The former was pushed to crash into the side barriers and the latter stopped in the middle of the crossroads. As a result,material damages were caused. as shown in the diagram.4
 
0.2%
After examining and inspecting the accident's site, the following was revealed:- The vehicle that caused the accident was travelling from the south to the north, taking the right lane. The first afflicted vehicle was travelling from east to the west taking the the left lane. the second afflicted vehicle was travelling from the east to the west taking the right lane. and because the former vehicle was running the red light, the front side of it collided with the left front side of the first afflicted vehicle which in returns, drifted colliding with right front side of the the second afflicted vehicle (motorcycle). and they both drifted and collided with left front side of third afflicted vehicle. All the vehicles stopped in the middle of the crossroad. As a result, the driver of the vehicle that caused the accident, and the two passengers were injured. The passengers of the first afflicted vehicle and the driver of the second afflicted vehicle, also were injured. Adding to that, material damages were caused to both vehicles. everything was settled4
 
0.2%
After examining and inspecting the accident's site, the following was revealed:- The vehicle that caused the accident was travelling from the south to the north taking the second lane from the left. The driver was speeding in the rain, when the vehicle skidded to the left to crash from the front into the light pole and the traffic sign. as a result, the driver and the passenger were injured and were admitted to Sheikh Khalefa’s hospital and material damages were caused to vehicles and the state’s properties material damages.4
 
0.2%
Other values (2051)2112
97.1%
2021-02-28T18:18:11.595035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the47808
 
20.5%
and11026
 
4.7%
vehicle9135
 
3.9%
of8102
 
3.5%
accident6749
 
2.9%
to6426
 
2.8%
was6220
 
2.7%
from5063
 
2.2%
causing3845
 
1.7%
damaged3371
 
1.4%
Other values (3404)125032
53.7%

Most occurring characters

ValueCountFrequency (%)
233658
16.9%
e156598
11.3%
t119050
 
8.6%
i83291
 
6.0%
h80613
 
5.8%
a79627
 
5.8%
n73196
 
5.3%
d60513
 
4.4%
o58181
 
4.2%
c55653
 
4.0%
Other values (72)380514
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1075457
77.9%
Space Separator265564
 
19.2%
Other Punctuation20947
 
1.5%
Uppercase Letter8372
 
0.6%
Decimal Number8046
 
0.6%
Dash Punctuation1677
 
0.1%
Control279
 
< 0.1%
Final Punctuation212
 
< 0.1%
Open Punctuation161
 
< 0.1%
Close Punctuation160
 
< 0.1%
Other values (3)19
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e156598
14.6%
t119050
11.1%
i83291
 
7.7%
h80613
 
7.5%
a79627
 
7.4%
n73196
 
6.8%
d60513
 
5.6%
o58181
 
5.4%
c55653
 
5.2%
r54030
 
5.0%
Other values (16)254705
23.7%
ValueCountFrequency (%)
T3061
36.6%
A2519
30.1%
W779
 
9.3%
H621
 
7.4%
D249
 
3.0%
S245
 
2.9%
B210
 
2.5%
M94
 
1.1%
I90
 
1.1%
K67
 
0.8%
Other values (15)437
 
5.2%
ValueCountFrequency (%)
41585
19.7%
51302
16.2%
1775
9.6%
3733
9.1%
0718
8.9%
2692
8.6%
9594
 
7.4%
8581
 
7.2%
6568
 
7.1%
7498
 
6.2%
ValueCountFrequency (%)
,11424
54.5%
.6746
32.2%
:2154
 
10.3%
'570
 
2.7%
"31
 
0.1%
*6
 
< 0.1%
6
 
< 0.1%
?5
 
< 0.1%
/4
 
< 0.1%
\1
 
< 0.1%
ValueCountFrequency (%)
233658
88.0%
 31906
 
12.0%
ValueCountFrequency (%)
197
92.9%
15
 
7.1%
ValueCountFrequency (%)
-1677
100.0%
ValueCountFrequency (%)
(161
100.0%
ValueCountFrequency (%)
)160
100.0%
ValueCountFrequency (%)
15
100.0%
ValueCountFrequency (%)
+3
100.0%
ValueCountFrequency (%)
`1
100.0%
ValueCountFrequency (%)
279
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1083829
78.5%
Common297065
 
21.5%

Most frequent character per script

ValueCountFrequency (%)
e156598
14.4%
t119050
11.0%
i83291
 
7.7%
h80613
 
7.4%
a79627
 
7.3%
n73196
 
6.8%
d60513
 
5.6%
o58181
 
5.4%
c55653
 
5.1%
r54030
 
5.0%
Other values (41)263077
24.3%
ValueCountFrequency (%)
233658
78.7%
 31906
 
10.7%
,11424
 
3.8%
.6746
 
2.3%
:2154
 
0.7%
-1677
 
0.6%
41585
 
0.5%
51302
 
0.4%
1775
 
0.3%
3733
 
0.2%
Other values (21)5105
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1348755
97.7%
None31906
 
2.3%
Punctuation233
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
233658
17.3%
e156598
11.6%
t119050
 
8.8%
i83291
 
6.2%
h80613
 
6.0%
a79627
 
5.9%
n73196
 
5.4%
d60513
 
4.5%
o58181
 
4.3%
c55653
 
4.1%
Other values (67)348375
25.8%
ValueCountFrequency (%)
 31906
100.0%
ValueCountFrequency (%)
197
84.5%
15
 
6.4%
15
 
6.4%
6
 
2.6%

Location.1
Categorical

HIGH CARDINALITY

Distinct1252
Distinct (%)57.8%
Missing10
Missing (%)0.5%
Memory size231.9 KiB
The intersection of Al-Ittihad newspaper
 
23
Ahmed bin Hamed intersection
 
15
Al-Saada Street, Sheikh Saif intersection, after the passports authority
 
14
Al Sharqi Ring Road, above Al Saada Bridge - Inward
 
13
Intersection of the National Bank of Abu Dhabi
 
12
Other values (1247)
2088 

Length

Max length126
Median length50
Mean length52.0665127
Min length7

Characters and Unicode

Total characters112724
Distinct characters73
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique871 ?
Unique (%)40.2%

Sample

1st rowAirport Street, Tarkhees intersection
2nd rowEtihad Newspaper intersection
3rd rowMina Region, Al Mina Street
4th rowSheikh Rashed St.,, Pepsi intersection
5th rowOff Hazaa Bin Zayed Al-Awal Street, beside Al-Khayyam Shops
ValueCountFrequency (%)
The intersection of Al-Ittihad newspaper23
 
1.1%
Ahmed bin Hamed intersection15
 
0.7%
Al-Saada Street, Sheikh Saif intersection, after the passports authority14
 
0.6%
Al Sharqi Ring Road, above Al Saada Bridge - Inward13
 
0.6%
Intersection of the National Bank of Abu Dhabi12
 
0.6%
Pepsi cola intersection12
 
0.6%
Sharqi Ring Street, after the second tunnel, outward11
 
0.5%
Arabian Gulf Street11
 
0.5%
Wimpey intersection11
 
0.5%
Al Falah Street, before formerly General Command intersection10
 
0.5%
Other values (1242)2033
93.5%
(Missing)10
 
0.5%
2021-02-28T18:18:12.120121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
street1733
 
10.1%
al1361
 
7.9%
intersection1334
 
7.8%
the1052
 
6.1%
of743
 
4.3%
with392
 
2.3%
bin371
 
2.2%
area361
 
2.1%
zayed300
 
1.7%
gulf257
 
1.5%
Other values (730)9288
54.0%

Most occurring characters

ValueCountFrequency (%)
15042
13.3%
e12228
 
10.8%
t9671
 
8.6%
a8455
 
7.5%
r7542
 
6.7%
i7368
 
6.5%
n6077
 
5.4%
o5319
 
4.7%
l3896
 
3.5%
h3841
 
3.4%
Other values (63)33285
29.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter83540
74.1%
Space Separator15042
 
13.3%
Uppercase Letter11584
 
10.3%
Other Punctuation2013
 
1.8%
Dash Punctuation261
 
0.2%
Decimal Number122
 
0.1%
Open Punctuation72
 
0.1%
Close Punctuation72
 
0.1%
Final Punctuation14
 
< 0.1%
Math Symbol3
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e12228
14.6%
t9671
11.6%
a8455
10.1%
r7542
9.0%
i7368
8.8%
n6077
 
7.3%
o5319
 
6.4%
l3896
 
4.7%
h3841
 
4.6%
s3325
 
4.0%
Other values (16)15818
18.9%
ValueCountFrequency (%)
S2782
24.0%
A2465
21.3%
M813
 
7.0%
B664
 
5.7%
C606
 
5.2%
H448
 
3.9%
I440
 
3.8%
T439
 
3.8%
R383
 
3.3%
D362
 
3.1%
Other values (14)2182
18.8%
ValueCountFrequency (%)
137
30.3%
232
26.2%
321
17.2%
710
 
8.2%
67
 
5.7%
06
 
4.9%
94
 
3.3%
53
 
2.5%
42
 
1.6%
ValueCountFrequency (%)
,1927
95.7%
.23
 
1.1%
&16
 
0.8%
?16
 
0.8%
/15
 
0.7%
'12
 
0.6%
"4
 
0.2%
ValueCountFrequency (%)
15042
100.0%
ValueCountFrequency (%)
-261
100.0%
ValueCountFrequency (%)
(72
100.0%
ValueCountFrequency (%)
)72
100.0%
ValueCountFrequency (%)
14
100.0%
ValueCountFrequency (%)
+3
100.0%
ValueCountFrequency (%)
`1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin95124
84.4%
Common17600
 
15.6%

Most frequent character per script

ValueCountFrequency (%)
e12228
12.9%
t9671
 
10.2%
a8455
 
8.9%
r7542
 
7.9%
i7368
 
7.7%
n6077
 
6.4%
o5319
 
5.6%
l3896
 
4.1%
h3841
 
4.0%
s3325
 
3.5%
Other values (40)27402
28.8%
ValueCountFrequency (%)
15042
85.5%
,1927
 
10.9%
-261
 
1.5%
(72
 
0.4%
)72
 
0.4%
137
 
0.2%
232
 
0.2%
.23
 
0.1%
321
 
0.1%
&16
 
0.1%
Other values (13)97
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII112710
> 99.9%
Punctuation14
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
15042
13.3%
e12228
 
10.8%
t9671
 
8.6%
a8455
 
7.5%
r7542
 
6.7%
i7368
 
6.5%
n6077
 
5.4%
o5319
 
4.7%
l3896
 
3.5%
h3841
 
3.4%
Other values (62)33271
29.5%
ValueCountFrequency (%)
14
100.0%

Fasten seat belt
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size126.4 KiB
No
1184 
Yes
984 
??
 
7

Length

Max length3
Median length2
Mean length2.452413793
Min length2

Characters and Unicode

Total characters5334
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowYes
3rd rowNo
4th rowYes
5th rowNo
ValueCountFrequency (%)
No1184
54.4%
Yes984
45.2%
??7
 
0.3%
2021-02-28T18:18:12.544818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:18:12.691732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
no1184
54.4%
yes984
45.2%
7
 
0.3%

Most occurring characters

ValueCountFrequency (%)
N1184
22.2%
o1184
22.2%
Y984
18.4%
e984
18.4%
s984
18.4%
?14
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3152
59.1%
Uppercase Letter2168
40.6%
Other Punctuation14
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
o1184
37.6%
e984
31.2%
s984
31.2%
ValueCountFrequency (%)
N1184
54.6%
Y984
45.4%
ValueCountFrequency (%)
?14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5320
99.7%
Common14
 
0.3%

Most frequent character per script

ValueCountFrequency (%)
N1184
22.3%
o1184
22.3%
Y984
18.5%
e984
18.5%
s984
18.5%
ValueCountFrequency (%)
?14
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5334
100.0%

Most frequent character per block

ValueCountFrequency (%)
N1184
22.2%
o1184
22.2%
Y984
18.4%
e984
18.4%
s984
18.4%
?14
 
0.3%

Road speed limit
Real number (ℝ≥0)

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.23678161
Minimum20
Maximum120
Zeros0
Zeros (%)0.0%
Memory size17.1 KiB
2021-02-28T18:18:12.810667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q160
median60
Q360
95-th percentile100
Maximum120
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.39570839
Coefficient of variation (CV)0.259274871
Kurtosis1.366660401
Mean63.23678161
Median Absolute Deviation (MAD)0
Skewness0.1950674369
Sum137540
Variance268.8192537
MonotocityNot monotonic
2021-02-28T18:18:12.986586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
601393
64.0%
80361
 
16.6%
40184
 
8.5%
100156
 
7.2%
2076
 
3.5%
1205
 
0.2%
ValueCountFrequency (%)
2076
 
3.5%
40184
 
8.5%
601393
64.0%
80361
 
16.6%
100156
 
7.2%
1205
 
0.2%
ValueCountFrequency (%)
1205
 
0.2%
100156
 
7.2%
80361
 
16.6%
601393
64.0%
40184
 
8.5%
2076
 
3.5%

Pedestrian action
Categorical

HIGH CORRELATION

Distinct20
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size138.6 KiB
None
1787 
Cross, there's no crosswalk
 
146
Cross, but not on the crosswalk
 
77
Stop
 
47
Cross on the crosswalk of the intersection
 
39
Other values (15)
 
79

Length

Max length47
Median length4
Mean length8.193103448
Min length4

Characters and Unicode

Total characters17820
Distinct characters29
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.2%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone
ValueCountFrequency (%)
None1787
82.2%
Cross, there's no crosswalk146
 
6.7%
Cross, but not on the crosswalk77
 
3.5%
Stop47
 
2.2%
Cross on the crosswalk of the intersection39
 
1.8%
Cross on crosswalk not in the intersection19
 
0.9%
Cross without attention15
 
0.7%
Other (specify)9
 
0.4%
Cross but not on the crosswalk 8
 
0.4%
Cross on crosswalk of the intersection6
 
0.3%
Other values (10)22
 
1.0%
2021-02-28T18:18:13.408345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none1787
50.1%
cross318
 
8.9%
crosswalk310
 
8.7%
the218
 
6.1%
on170
 
4.8%
there's146
 
4.1%
no146
 
4.1%
not111
 
3.1%
but88
 
2.5%
intersection76
 
2.1%
Other values (14)196
 
5.5%

Most occurring characters

ValueCountFrequency (%)
o3065
17.2%
e2489
14.0%
n2419
13.6%
N1787
10.0%
s1511
8.5%
1400
7.9%
r872
 
4.9%
t856
 
4.8%
c399
 
2.2%
h390
 
2.2%
Other values (19)2632
14.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13842
77.7%
Uppercase Letter2177
 
12.2%
Space Separator1400
 
7.9%
Other Punctuation379
 
2.1%
Open Punctuation11
 
0.1%
Close Punctuation11
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
o3065
22.1%
e2489
18.0%
n2419
17.5%
s1511
10.9%
r872
 
6.3%
t856
 
6.2%
c399
 
2.9%
h390
 
2.8%
a333
 
2.4%
w327
 
2.4%
Other values (10)1181
 
8.5%
ValueCountFrequency (%)
N1787
82.1%
C326
 
15.0%
S53
 
2.4%
O11
 
0.5%
ValueCountFrequency (%)
,233
61.5%
'146
38.5%
ValueCountFrequency (%)
1400
100.0%
ValueCountFrequency (%)
(11
100.0%
ValueCountFrequency (%)
)11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16019
89.9%
Common1801
 
10.1%

Most frequent character per script

ValueCountFrequency (%)
o3065
19.1%
e2489
15.5%
n2419
15.1%
N1787
11.2%
s1511
9.4%
r872
 
5.4%
t856
 
5.3%
c399
 
2.5%
h390
 
2.4%
a333
 
2.1%
Other values (14)1898
11.8%
ValueCountFrequency (%)
1400
77.7%
,233
 
12.9%
'146
 
8.1%
(11
 
0.6%
)11
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII17820
100.0%

Most frequent character per block

ValueCountFrequency (%)
o3065
17.2%
e2489
14.0%
n2419
13.6%
N1787
10.0%
s1511
8.5%
1400
7.9%
r872
 
4.9%
t856
 
4.8%
c399
 
2.2%
h390
 
2.2%
Other values (19)2632
14.8%

Number of Lanes
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)0.3%
Missing78
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean3.367668097
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size17.1 KiB
2021-02-28T18:18:13.975001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.017857702
Coefficient of variation (CV)0.3022440671
Kurtosis0.4784789663
Mean3.367668097
Median Absolute Deviation (MAD)1
Skewness-0.5707948519
Sum7062
Variance1.036034302
MonotocityNot monotonic
2021-02-28T18:18:14.120123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4941
43.3%
3668
30.7%
2189
 
8.7%
1151
 
6.9%
5125
 
5.7%
621
 
1.0%
72
 
0.1%
(Missing)78
 
3.6%
ValueCountFrequency (%)
1151
 
6.9%
2189
 
8.7%
3668
30.7%
4941
43.3%
5125
 
5.7%
621
 
1.0%
72
 
0.1%
ValueCountFrequency (%)
72
 
0.1%
621
 
1.0%
5125
 
5.7%
4941
43.3%
3668
30.7%
2189
 
8.7%
1151
 
6.9%

Interactions

2021-02-28T18:17:39.388216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:39.611506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:39.819371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:39.993551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:40.200018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:40.403787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:40.608712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:40.801613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:40.984501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:41.171392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:41.400261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:41.590151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:41.786057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:41.974932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:42.146893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:42.321234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:42.679030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:42.861927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:43.041823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:43.274692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:43.471578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:43.760414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:43.981305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:44.184145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:44.355807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:44.589672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:44.837531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:45.102380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:45.303267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:45.480164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-28T18:18:14.335773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-28T18:18:14.669563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-28T18:18:14.979406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-28T18:18:15.422153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-28T18:18:16.113072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-28T18:17:46.184197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-28T18:17:48.662642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-28T18:17:49.509887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-28T18:17:49.796745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

EmiratePolice StationYearMonthDateCityRoadReport NumberDayWeekReport typeReasonsComputedStreetsStreetPlaceLocationlightingWeatherRoad surfaceIntersectionAccident TypeNationalitiesSeat BeltInjured person positionInjured person's seatDegree of the injuryAge of the injuredGenderGender of the injuredInp AgeAge GroupDate - MonthNationality of the Injured personDay.1Iac Rep TimeTimeAge Group - MinistryComputed2AreaBlockStr CodeAccident DescriptionLocation.1Fasten seat beltRoad speed limitPedestrian actionNumber of Lanes
0Abu DhabiShaabiya Police Station2011January01-01-11Abu DhabiInternal road1.000000e+111First weekTraffic accident with injuriesunder influence of intoxicant or narcotic drug1Rashid Bin Saeed Al Maktoum intersection - Street IP28A 27Rashid Bin Saeed Al Maktoum intersection - Street IP28A 27Govt. authorityIntersectionnight - enough lightsclearDryPerpendicular IntersectionPerpendicular collisionSyriaYPassengerFront passenger seatMinor47FFemale4746 to 60 yearsJanArab StatesSaturday0:00PM46 to 60 years1MushrifBlock 23-21873803.                   After detection and inspecting the accident site, the following accident chart became clear: While the causing vehicle was coming from the west towards the east and it would like to turn left, the lane is on the far left, the damaged vehicle is coming from the east towards the west and the lane is on the far right. In the case of moving forward, and because of bypassing the red light and driving drunk the causing vehicle was collided from the front right corner. The damaged vehicle is on the right side of the causing vehicle. The accident resulted in a female passenger being injured in the damaged vehicle, as well as material damage to both vehicles. The detection and necessary measures were taken.Airport Street, Tarkhees intersectionYes60None4.0
1Abu DhabiShaabiya Police Station2011January01-01-11Abu DhabiInternal road1.000000e+111First weekTraffic accident with injuriesunder influence of intoxicant or narcotic drug1Al Sharqi intersection (Muroor) - Mohamed bin Khalifa IP49AAl Sharqi intersection (Muroor) - Mohamed bin Khalifa IP49ACommercial areaIntersectionnight - enough lightsclearDryPerpendicular IntersectionSide collisionIndiaYPassengerFront passenger seatMinor56MMale5646 to 60 yearsJanAsian StatesSaturday0:35PM46 to 60 years1Al WahdaBlock 17-11743804.                   After detection and inspection by the accident chart, the following is revealed: -The causing vehicle was coming from the west towards the east with the left lane line, the damaged vehicle was coming from the south towards the north with the right lane line, while the two vehicles were passing and because of bypassing the red traffic light by the offending driver hit the front of the damaged vehicle on the right side of the damaged vehicle, and accordingly the accident was charted.Etihad Newspaper intersectionYes60None3.0
2Abu DhabiAl Madina Police Station2011January01-01-11Abu DhabiInternal road1.000000e+111First weekTraffic accident with injuriesunder influence of intoxicant or narcotic drug1Street 10Street 10Commercial areaHighwaynight - enough lightsclearDryNot on IntersectionHitting a stationary object off the roadUAENPassengerFront passenger seatSevere32MMale3231 to 45 yearsJanUAESaturday4:20PM31 to 45 years1MinaBlock 73-1433805.                   After detection and inspecting the accident site, the following accident chart became clear: The causing vehicle was coming from the south to the north, and due to the driving in an abnormal state (drunkenness) by the offending driver, the vehicle deviated from the road and hit the front of the causing vehicle with a billboard column. The accident was charted and necessary action was taken.Mina Region, Al Mina StreetNo60None5.0
3Abu DhabiKhaldiya Police Station2011January01-01-11Abu DhabiInternal road1.000000e+111First weekTraffic accident with injuriesBypassing the red traffic light1Rashed bin Saeed Al-Maktoum intersection - Street 29 IP76Rashed bin Saeed Al-Maktoum intersection - Street 29 IP76Commercial areaIntersectiondayclearDryPerpendicular IntersectionSide collisionYemenYPassengerFront passenger seatMinor41FFemale4131 to 45 yearsJanGCCSaturday16:19PM31 to 45 years1MushrifBlock 24-21883806.                   After detection and inspecting the accident site, it became clear that the following accident chart: - The causing vehicle is coming from the south to the north, heading to the west, the path is passed by the arrow, and the damaged vehicle is coming from the north to the south, on the right path. To bypass the red light by the causing vehicle, the front of the damaged vehicle collided with the right side of the causing vehicle. The accident was charted and the crisis was conducted.Sheikh Rashed St.,, Pepsi intersectionYes60None3.0
4Abu DhabiKhaldiya Police Station2011January01-01-11Abu DhabiInternal road1.000000e+111First weekTraffic accident with injuriesNegligence and inobservance1Khalifa Bin Shakhbout Street (28)Khalifa Bin Shakhbout Street (28)OtherSingle roadnight - enough lightsclearDryNot on IntersectionHitting a stationary object on the roadUAENPassengerBack passenger seatModerate39FFemale3931 to 45 yearsJanUAESaturday22:27PM31 to 45 years1MushrifBlock 38-2183807.                   After detection and inspecting the accident site, the following accident chart became clear: During the rotation of the causing vehicle from the filter triangle, the driver of the causing vehicle could not control the causing vehicle, which led to hitting the front of the causing vehicle on the sidewalk leading to its rollover on its left side. The accident was charted and necessary action was taken.NaNNo40None1.0
5Abu DhabiKhaldiya Police Station2011January01-01-11Abu DhabiInternal road1.000000e+111First weekTraffic accident with injuriesNegligence and inobservance1Khalifa Bin Shakhbout Street (28)Khalifa Bin Shakhbout Street (28)OtherSingle roadnight - enough lightsclearDryNot on IntersectionHitting a stationary object on the roadUAENPassengerBack passenger seatMinor17FFemale178 to 17 yearsJanUAESaturday22:27PMBelow 18 years1MushrifBlock 38-2183808.                   After detection and inspecting the accident site, the following accident chart became clear: - During the rotation of the causing vehicle from the filter triangle, the driver of the causing vehicle could not control the causing vehicle, which led to hitting the front of the causing vehicle on the sidewalk leading to its rollover on its left side. The accident was charted and necessary action was taken.NaNNo40None1.0
6Abu DhabiKhaldiya Police Station2011January01-01-11Abu DhabiInternal road1.000000e+111First weekTraffic accident with injuriesNegligence and inobservance1Khalifa Bin Shakhbout Street (28)Khalifa Bin Shakhbout Street (28)OtherSingle roadnight - enough lightsclearDryNot on IntersectionHitting a stationary object on the roadUAENDriverDriver's seatMinor15MMale158 to 17 yearsJanUAESaturday22:27PMBelow 18 years1MushrifBlock 38-2183809.                   After detection and inspecting the accident site, the following accident chart became clear: During the rotation of the causing vehicle from the filter triangle, the driver of the causing vehicle could not control the causing vehicle, which led to hitting the front of the causing vehicle on the sidewalk leading to its rollover on its left side. The accident was charted and necessary action was taken.NaNNo40None1.0
7Abu DhabiShaabiya Police Station2011January02-01-11Abu DhabiInternal road1.000000e+112First weekTraffic accident with injuriesSpeedy without taking road conditions into account1Hazaa Bin Zayed Al Awal Street (11)Hazaa Bin Zayed Al Awal Street (11)Commercial areaHighwaydayclearDryNot on or around 20 metersPerson run overIndiaNPedestrianNo passengerModerate31MMale3131 to 45 yearsJanAsian StatesSunday12:08PM31 to 45 years1Dhafra (Abu Dhabi)Block 55-1233810.                   After detection and inspecting the accident site, the following accident chart became clear: - The first causing vehicle is coming from the south towards the north, and run-over person (second offender) is coming from the north towards the south, and because of the lack of estimation of the road users by the first offender and passing the run-over person (second offender) the road away from the pedestrian, the right-side mirror collided with the run-over person, and the accident was charted and the crisis was conducted.Off Hazaa Bin Zayed Al-Awal Street, beside Al-Khayyam ShopsNo40Cross but not on the crosswalk2.0
8Abu DhabiShaabiya Police Station2011January02-01-11Abu DhabiInternal road1.000000e+112First weekTraffic accident with injuriesNot leaving enough space1Al Sharqi Street (Muroor) (4)Al Sharqi Street (Muroor) (4)Govt. authorityIntersectiondayclearDryPerpendicular IntersectionRear collisionPakistanNDriverDriver's seatModerate26MMale2618 to 30 yearsJanAsian StatesSunday15:40PM18 to 30 years1Hadbat Al ZafaranBlock 45-1133811.                   After detection and inspecting the accident chart, the policeman of the accident chart revealed the following: - While the causing vehicle was coming from the east to the west, taking the middle path, and the damaged vehicle in front of it on the same lane, and because of not leaving enough space with the offending driver, the front of the causing vehicle collided with the rear end of the damaged vehicle and the accident resulted in the injured driver being moderately injured. He received the necessary treatment and left the hospital, as a result of damage. It leaded to financial losses also, then the site of the accident was examined and necessary action was taken.Zaafarana area, Al-Moroor Street before civil defence intersection - OutwardNo60None4.0
9Abu DhabiShaabiya Police Station2011January03-01-11Abu DhabiInternal road1.000000e+113First weekTraffic accident with injuriesSpeedy without taking road conditions into account1Al Saada Intersection - Salam IP69Al Saada Intersection - Salam IP69Bridge / TunnelBridgedayclearDryNot on or around 20 metersMultiple collisionPakistanNPassengerBack passenger seatMinor26MMale2618 to 30 yearsJanAsian StatesMonday5:30PM18 to 30 years1Qasr Al BaharBlock 36-11293812.                   After detection and inspecting the accident site, the following accident chart clear: - While all the vehicles that are involved in the accident were coming from the east to the west moving in the far-right lane and upon their arrival over the Al-Saada Bridge, the fourth damaged vehicle was surprised by stalled vehicle on the road and he lowered its speed, and the first, second and third damaged vehicles stopped in the far-right lane and due to the speed without taking into account circumstances and not slowing down upon arrival near the bridge, then the front of his vehicle collided from the left side with the rear of the first damaged vehicle, and as a result of the collision, it rushed and collided with the rear of the second damaged vehicle, and the second damaged vehicle rushed and collided with the third damaged vehicle and the third damaged vehicle ran into the fourth damaged vehicle, according to the chart, the accident occurred.Al Sharqi Ring Road, above Al Saada Bridge - InwardNo100None5.0

Last rows

EmiratePolice StationYearMonthDateCityRoadReport NumberDayWeekReport typeReasonsComputedStreetsStreetPlaceLocationlightingWeatherRoad surfaceIntersectionAccident TypeNationalitiesSeat BeltInjured person positionInjured person's seatDegree of the injuryAge of the injuredGenderGender of the injuredInp AgeAge GroupDate - MonthNationality of the Injured personDay.1Iac Rep TimeTimeAge Group - MinistryComputed2AreaBlockStr CodeAccident DescriptionLocation.1Fasten seat beltRoad speed limitPedestrian actionNumber of Lanes
2165Abu DhabiKhaldiya Police Station2013June29-06-13Abu DhabiInternal road1.000000e+1129Fifth weekTraffic accident with injuriesunder influence of intoxicant or narcotic drug1Street Arabian Gulf(30)Street Arabian Gulf(30)Bridge / TunnelBridge / Tunnelnight - enough lightsclearDryNot on IntersectionRear collisionUAENPassengerBack passenger seatModerate1FFemale11 to 7 yearsJunUAESaturday22:03PMBelow 18 years1Musaffah BridgeNot Identified165666.                  After examining and inspecting the accident's site, the following was revealed:Arabian Gulf Street, outward, on Mussafah BridgeNo100None4.0
2166Abu DhabiKhaldiya Police Station2013June29-06-13Abu DhabiInternal road1.000000e+1129Fifth weekTraffic accident with injuriesunder influence of intoxicant or narcotic drug1Street Arabian Gulf(30)Street Arabian Gulf(30)Bridge / TunnelBridge / Tunnelnight - enough lightsclearDryNot on IntersectionRear collisionUAENPassengerBack passenger seatSevere2MMale21 to 7 yearsJunUAESaturday22:03PMBelow 18 years1Musaffah BridgeNot Identified16While the two vehicles were traveling from the west towards the east in the left lane, and while the vehicle was causing the rear and the damaged one was in the front, and while they were driving, because of the driver of the causing vehicle under the influence of alcoholic drinks and speed without taking into account the road conditions, the accidentArabian Gulf Street, outward, on Mussafah BridgeNo100None4.0
2167Abu DhabiKhaldiya Police Station2013June29-06-13Abu DhabiInternal road1.000000e+1129Fifth weekTraffic accident with injuriesunder influence of intoxicant or narcotic drug1Street Arabian Gulf(30)Street Arabian Gulf(30)Bridge / TunnelBridge / Tunnelnight - enough lightsclearDryNot on IntersectionRear collisionUAENPassengerBack passenger seatSevere27FFemale2718 to 30 yearsJunUAESaturday22:03PM18 to 30 years1Musaffah BridgeNot Identified16Where the front of the causing vehicle collided at the back of the damaged vehicle on the right side, and from the force of the collision, the driver of the damaged vehicle lost control of his vehicle, so it swerved to the far left and went up to the agricultural island separating the two directions, then got off it and turned around itselfArabian Gulf Street, outward, on Mussafah BridgeNo100None4.0
2168Abu DhabiKhaldiya Police Station2013June30-06-13Abu DhabiInternal road1.000000e+1130Fifth weekTraffic accident with injuriesNot leaving enough space1IP127 IntersectionDelma - Street 32IP127 IntersectionDelma - Street 32Residential areaDouble roadnight - enough lightsclearDryPerpendicular IntersectionRear collisionUAEYDriverDriver's seatMinor20MMale2018 to 30 yearsJunUAESunday8:35AM18 to 30 years1Al ButainBlock 96-2173And stabilized in the second lane, against the direction of traffic, how many permanent turns around itselfSheikh Sultan Bin Zayed Street with Delma Street, Al Bateen AreaYes60None4.0
2169Abu DhabiKhaldiya Police Station2013June30-06-13Abu DhabiInternal road1.000000e+1130Fifth weekTraffic accident with injuriesNot leaving enough space1IP127 IntersectionDelma - Street 32IP127 IntersectionDelma - Street 32Residential areaDouble roadnight - enough lightsclearDryPerpendicular IntersectionRear collisionUAEYPassengerFront passenger seatModerate19MMale1918 to 30 yearsJunUAESunday8:35AM18 to 30 years1Al ButainBlock 96-2173And it stabilized against the direction of traffic on the far right. The accident resulted in bodily injuries, damage to state property and material damage to the two vehicles, and According to diagramed, the accident was occurredSheikh Sultan Bin Zayed Street with Delma Street, Al Bateen AreaYes60None4.0
2170Abu DhabiShaabiya Police Station2013June30-06-13Abu DhabiInternal road1.000000e+1130Fifth weekTraffic accident with injuriesBypassing the red traffic light1Al Sharqi Street (Muroor) (4)Al Sharqi Street (Muroor) (4)Govt. authorityIntersectiondayclearDryPerpendicular IntersectionPerpendicular collisionUAENPassengerBack passenger seatModerate24FFemale2418 to 30 yearsJunUAESunday12:15PM18 to 30 years1Hadbat Al ZafaranBlock 20-1135667.                  After examining and inspecting the accident's site, the following was revealed:Police College intersectionNo80None4.0
2171Abu DhabiShaabiya Police Station2013June30-06-13Abu DhabiInternal road1.000000e+1130Fifth weekTraffic accident with injuriesBypassing the red traffic light1Al Sharqi Street (Muroor) (4)Al Sharqi Street (Muroor) (4)Govt. authorityIntersectiondayclearDryPerpendicular IntersectionPerpendicular collisionUAENPassengerFront passenger seatModerate49FFemale4946 to 60 yearsJunUAESunday12:15PM46 to 60 years1Hadbat Al ZafaranBlock 20-113While the two vehicles were traveling from the west towards the east in the left lane, and while the vehicle was causing the rear and the damaged one was in the front, and while they were driving, because of the driver of the causing vehicle under the influence of alcoholic drinks and speed without taking into account the road conditions, the accidentPolice College intersectionNo80None4.0
2172Abu DhabiShaabiya Police Station2013June30-06-13Abu DhabiInternal road1.000000e+1130Fifth weekTraffic accident with injuriesBypassing the red traffic light1Al Sharqi Street (Muroor) (4)Al Sharqi Street (Muroor) (4)Govt. authorityIntersectiondayclearDryPerpendicular IntersectionPerpendicular collisionUAEYDriverDriver's seatMinor22FFemale2218 to 30 yearsJunUAESunday12:15PM18 to 30 years1Hadbat Al ZafaranBlock 20-113Where the front of the causing vehicle collided at the back of the damaged vehicle on the right side, and from the force of the collision, the driver of the damaged vehicle lost control of his vehicle, so it swerved to the far left and went up to the agricultural island separating the two directions, then got off it and turned around itselfPolice College intersectionYes80None4.0
2173Abu DhabiShaabiya Police Station2013June30-06-13Abu DhabiInternal road1.000000e+1130Fifth weekTraffic accident with injuriesBypassing the red traffic light1Al Sharqi Street (Muroor) (4)Al Sharqi Street (Muroor) (4)Govt. authorityIntersectiondayclearDryPerpendicular IntersectionPerpendicular collisionUAENPassengerBack passenger seatMinor1MMale11 to 7 yearsJunUAESunday12:15PMBelow 18 years1Hadbat Al ZafaranBlock 20-113And stabilized in the second lane, against the direction of traffic, how many permanent turns around itselfPolice College intersectionNo80None4.0
2174Abu DhabiShaabiya Police Station2013June30-06-13Abu DhabiInternal road1.000000e+1130NaNTraffic accident with injuriesBypassing the red traffic light1Al Sharqi Street (Muroor) (4)Al Sharqi Street (Muroor) (4)Govt. authorityIntersectiondayclearDryPerpendicular IntersectionPerpendicular collisionUAEYPassengerBack passenger seatModerate21FFemale2118 to 30 yearsJunUAESunday12:15PM18 to 30 years1Hadbat Al ZafaranBlock 20-113And it stabilized against the direction of traffic on the far right. The accident resulted in bodily injuries, damage to state property and material damage to the two vehicles, and According to diagramed, the accident was occurredPolice College intersectionYes80None4.0

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EmiratePolice StationYearMonthDateCityRoadReport NumberDayWeekReport typeReasonsComputedStreetsStreetPlaceLocationlightingWeatherRoad surfaceIntersectionAccident TypeNationalitiesSeat BeltInjured person positionInjured person's seatDegree of the injuryAge of the injuredGenderGender of the injuredInp AgeAge GroupDate - MonthNationality of the Injured personDay.1Iac Rep TimeTimeAge Group - MinistryComputed2AreaBlockStr CodeAccident DescriptionLocation.1Fasten seat beltRoad speed limitPedestrian actionNumber of Lanescount
0Abu DhabiShaabiya Police Station2013February16-02-13Abu DhabiInternal road1.000000e+1116Third weekTraffic accident with injuriesNot leaving enough space1Street Salam (8)Street Salam (8)Govt. authorityHighwaydayclearDryNot on IntersectionRear collisionBangladeshNPassengerBack passenger seatMinor31MMale3131 to 45 yearsFebAsian StatesSaturday17:30PM31 to 45 years1Hadbat Al ZafaranBlock 40-112After examining and inspecting the accident's site, the following was revealed:- \nthe vehicles were travelling from the west to the east taking the second lane from the right. The vehicle that caused the accident didn’t keep a distance from the front vehicle and collided from the front side with the back side of the first afflicted vehicle. Leading the latter to collide from the front side with back side of the second afflicted vehicle and then leading the second to collide from the front side with back side of the third afflicted vehicle. as a result, the passenger of the former vehicle was injured and material damagers were caused to the vehicles. As shown in the diagram.Sharqi Ring Street, after the second tunnel, outwardNo100None4.03